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A Knowledge Graph-Based Search Engine for Robustly Finding Doctors and Locations in the Healthcare Domain (2310.05258v1)

Published 8 Oct 2023 in cs.AI, cs.DB, and cs.IR

Abstract: Efficiently finding doctors and locations is an important search problem for patients in the healthcare domain, for which traditional information retrieval methods tend not to work optimally. In the last ten years, knowledge graphs (KGs) have emerged as a powerful way to combine the benefits of gleaning insights from semi-structured data using semantic modeling, natural language processing techniques like information extraction, and robust querying using structured query languages like SPARQL and Cypher. In this short paper, we present a KG-based search engine architecture for robustly finding doctors and locations in the healthcare domain. Early results demonstrate that our approach can lead to significantly higher coverage for complex queries without degrading quality.

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References (20)
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[2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Liang, F., Qiang, R., Yang, J.: Exploiting real-time information retrieval in the microblogosphere. In: Proceedings of the 12th ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 267–276 (2012) Kejriwal and Szekely [2017a] Kejriwal, M., Szekely, P.: Supervised typing of big graphs using semantic embeddings. In: Proceedings of the International Workshop on Semantic Big Data, pp. 1–6 (2017) Kejriwal and Szekely [2017b] Kejriwal, M., Szekely, P.: Scalable generation of type embeddings using the abox. Open Journal of Semantic Web (OJSW) 4(1), 20–34 (2017) Kejriwal et al. [2021] Kejriwal, M., Knoblock, C.A., Szekely, P.: Knowledge Graphs: Fundamentals, Techniques, and Applications. MIT Press, ??? (2021) Schulz and Martínez-Costa [2013] Schulz, S., Martínez-Costa, C.: How ontologies can improve semantic interoperability in health care. In: International Workshop on Process-oriented Information Systems in Healthcare, pp. 1–10 (2013). Springer Grishman [2015] Grishman, R.: Information extraction. IEEE Intelligent Systems 30(5), 8–15 (2015) Kejriwal [2019] Kejriwal, M.: Information extraction. Domain-Specific Knowledge Graph Construction, 9–31 (2019) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: Information extraction in illicit web domains. In: Proceedings of the 26th International Conference on World Wide Web, pp. 997–1006 (2017) Tang and Kejriwal [2023] Tang, Z., Kejriwal, M.: Evaluating deep generative models on cognitive tasks: a case study. Discover Artificial Intelligence 3(1), 21 (2023) Liu et al. [2009] Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Szekely, P.: Supervised typing of big graphs using semantic embeddings. In: Proceedings of the International Workshop on Semantic Big Data, pp. 1–6 (2017) Kejriwal and Szekely [2017b] Kejriwal, M., Szekely, P.: Scalable generation of type embeddings using the abox. Open Journal of Semantic Web (OJSW) 4(1), 20–34 (2017) Kejriwal et al. [2021] Kejriwal, M., Knoblock, C.A., Szekely, P.: Knowledge Graphs: Fundamentals, Techniques, and Applications. MIT Press, ??? (2021) Schulz and Martínez-Costa [2013] Schulz, S., Martínez-Costa, C.: How ontologies can improve semantic interoperability in health care. In: International Workshop on Process-oriented Information Systems in Healthcare, pp. 1–10 (2013). Springer Grishman [2015] Grishman, R.: Information extraction. IEEE Intelligent Systems 30(5), 8–15 (2015) Kejriwal [2019] Kejriwal, M.: Information extraction. Domain-Specific Knowledge Graph Construction, 9–31 (2019) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: Information extraction in illicit web domains. In: Proceedings of the 26th International Conference on World Wide Web, pp. 997–1006 (2017) Tang and Kejriwal [2023] Tang, Z., Kejriwal, M.: Evaluating deep generative models on cognitive tasks: a case study. Discover Artificial Intelligence 3(1), 21 (2023) Liu et al. [2009] Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Szekely, P.: Scalable generation of type embeddings using the abox. Open Journal of Semantic Web (OJSW) 4(1), 20–34 (2017) Kejriwal et al. [2021] Kejriwal, M., Knoblock, C.A., Szekely, P.: Knowledge Graphs: Fundamentals, Techniques, and Applications. MIT Press, ??? (2021) Schulz and Martínez-Costa [2013] Schulz, S., Martínez-Costa, C.: How ontologies can improve semantic interoperability in health care. In: International Workshop on Process-oriented Information Systems in Healthcare, pp. 1–10 (2013). Springer Grishman [2015] Grishman, R.: Information extraction. IEEE Intelligent Systems 30(5), 8–15 (2015) Kejriwal [2019] Kejriwal, M.: Information extraction. Domain-Specific Knowledge Graph Construction, 9–31 (2019) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: Information extraction in illicit web domains. In: Proceedings of the 26th International Conference on World Wide Web, pp. 997–1006 (2017) Tang and Kejriwal [2023] Tang, Z., Kejriwal, M.: Evaluating deep generative models on cognitive tasks: a case study. Discover Artificial Intelligence 3(1), 21 (2023) Liu et al. [2009] Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Knoblock, C.A., Szekely, P.: Knowledge Graphs: Fundamentals, Techniques, and Applications. MIT Press, ??? (2021) Schulz and Martínez-Costa [2013] Schulz, S., Martínez-Costa, C.: How ontologies can improve semantic interoperability in health care. In: International Workshop on Process-oriented Information Systems in Healthcare, pp. 1–10 (2013). Springer Grishman [2015] Grishman, R.: Information extraction. IEEE Intelligent Systems 30(5), 8–15 (2015) Kejriwal [2019] Kejriwal, M.: Information extraction. Domain-Specific Knowledge Graph Construction, 9–31 (2019) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: Information extraction in illicit web domains. In: Proceedings of the 26th International Conference on World Wide Web, pp. 997–1006 (2017) Tang and Kejriwal [2023] Tang, Z., Kejriwal, M.: Evaluating deep generative models on cognitive tasks: a case study. Discover Artificial Intelligence 3(1), 21 (2023) Liu et al. [2009] Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Schulz, S., Martínez-Costa, C.: How ontologies can improve semantic interoperability in health care. In: International Workshop on Process-oriented Information Systems in Healthcare, pp. 1–10 (2013). Springer Grishman [2015] Grishman, R.: Information extraction. IEEE Intelligent Systems 30(5), 8–15 (2015) Kejriwal [2019] Kejriwal, M.: Information extraction. Domain-Specific Knowledge Graph Construction, 9–31 (2019) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: Information extraction in illicit web domains. In: Proceedings of the 26th International Conference on World Wide Web, pp. 997–1006 (2017) Tang and Kejriwal [2023] Tang, Z., Kejriwal, M.: Evaluating deep generative models on cognitive tasks: a case study. Discover Artificial Intelligence 3(1), 21 (2023) Liu et al. [2009] Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Grishman, R.: Information extraction. IEEE Intelligent Systems 30(5), 8–15 (2015) Kejriwal [2019] Kejriwal, M.: Information extraction. Domain-Specific Knowledge Graph Construction, 9–31 (2019) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: Information extraction in illicit web domains. In: Proceedings of the 26th International Conference on World Wide Web, pp. 997–1006 (2017) Tang and Kejriwal [2023] Tang, Z., Kejriwal, M.: Evaluating deep generative models on cognitive tasks: a case study. Discover Artificial Intelligence 3(1), 21 (2023) Liu et al. [2009] Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M.: Information extraction. Domain-Specific Knowledge Graph Construction, 9–31 (2019) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: Information extraction in illicit web domains. In: Proceedings of the 26th International Conference on World Wide Web, pp. 997–1006 (2017) Tang and Kejriwal [2023] Tang, Z., Kejriwal, M.: Evaluating deep generative models on cognitive tasks: a case study. Discover Artificial Intelligence 3(1), 21 (2023) Liu et al. [2009] Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Szekely, P.: Information extraction in illicit web domains. In: Proceedings of the 26th International Conference on World Wide Web, pp. 997–1006 (2017) Tang and Kejriwal [2023] Tang, Z., Kejriwal, M.: Evaluating deep generative models on cognitive tasks: a case study. Discover Artificial Intelligence 3(1), 21 (2023) Liu et al. [2009] Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Tang, Z., Kejriwal, M.: Evaluating deep generative models on cognitive tasks: a case study. Discover Artificial Intelligence 3(1), 21 (2023) Liu et al. [2009] Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. 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Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. 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In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Szekely, P.: Supervised typing of big graphs using semantic embeddings. In: Proceedings of the International Workshop on Semantic Big Data, pp. 1–6 (2017) Kejriwal and Szekely [2017b] Kejriwal, M., Szekely, P.: Scalable generation of type embeddings using the abox. Open Journal of Semantic Web (OJSW) 4(1), 20–34 (2017) Kejriwal et al. [2021] Kejriwal, M., Knoblock, C.A., Szekely, P.: Knowledge Graphs: Fundamentals, Techniques, and Applications. MIT Press, ??? (2021) Schulz and Martínez-Costa [2013] Schulz, S., Martínez-Costa, C.: How ontologies can improve semantic interoperability in health care. In: International Workshop on Process-oriented Information Systems in Healthcare, pp. 1–10 (2013). Springer Grishman [2015] Grishman, R.: Information extraction. IEEE Intelligent Systems 30(5), 8–15 (2015) Kejriwal [2019] Kejriwal, M.: Information extraction. Domain-Specific Knowledge Graph Construction, 9–31 (2019) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: Information extraction in illicit web domains. In: Proceedings of the 26th International Conference on World Wide Web, pp. 997–1006 (2017) Tang and Kejriwal [2023] Tang, Z., Kejriwal, M.: Evaluating deep generative models on cognitive tasks: a case study. Discover Artificial Intelligence 3(1), 21 (2023) Liu et al. [2009] Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Szekely, P.: Scalable generation of type embeddings using the abox. Open Journal of Semantic Web (OJSW) 4(1), 20–34 (2017) Kejriwal et al. [2021] Kejriwal, M., Knoblock, C.A., Szekely, P.: Knowledge Graphs: Fundamentals, Techniques, and Applications. MIT Press, ??? (2021) Schulz and Martínez-Costa [2013] Schulz, S., Martínez-Costa, C.: How ontologies can improve semantic interoperability in health care. In: International Workshop on Process-oriented Information Systems in Healthcare, pp. 1–10 (2013). Springer Grishman [2015] Grishman, R.: Information extraction. IEEE Intelligent Systems 30(5), 8–15 (2015) Kejriwal [2019] Kejriwal, M.: Information extraction. Domain-Specific Knowledge Graph Construction, 9–31 (2019) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: Information extraction in illicit web domains. In: Proceedings of the 26th International Conference on World Wide Web, pp. 997–1006 (2017) Tang and Kejriwal [2023] Tang, Z., Kejriwal, M.: Evaluating deep generative models on cognitive tasks: a case study. Discover Artificial Intelligence 3(1), 21 (2023) Liu et al. [2009] Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Knoblock, C.A., Szekely, P.: Knowledge Graphs: Fundamentals, Techniques, and Applications. MIT Press, ??? (2021) Schulz and Martínez-Costa [2013] Schulz, S., Martínez-Costa, C.: How ontologies can improve semantic interoperability in health care. In: International Workshop on Process-oriented Information Systems in Healthcare, pp. 1–10 (2013). Springer Grishman [2015] Grishman, R.: Information extraction. IEEE Intelligent Systems 30(5), 8–15 (2015) Kejriwal [2019] Kejriwal, M.: Information extraction. Domain-Specific Knowledge Graph Construction, 9–31 (2019) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: Information extraction in illicit web domains. In: Proceedings of the 26th International Conference on World Wide Web, pp. 997–1006 (2017) Tang and Kejriwal [2023] Tang, Z., Kejriwal, M.: Evaluating deep generative models on cognitive tasks: a case study. Discover Artificial Intelligence 3(1), 21 (2023) Liu et al. [2009] Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Schulz, S., Martínez-Costa, C.: How ontologies can improve semantic interoperability in health care. In: International Workshop on Process-oriented Information Systems in Healthcare, pp. 1–10 (2013). Springer Grishman [2015] Grishman, R.: Information extraction. IEEE Intelligent Systems 30(5), 8–15 (2015) Kejriwal [2019] Kejriwal, M.: Information extraction. Domain-Specific Knowledge Graph Construction, 9–31 (2019) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: Information extraction in illicit web domains. In: Proceedings of the 26th International Conference on World Wide Web, pp. 997–1006 (2017) Tang and Kejriwal [2023] Tang, Z., Kejriwal, M.: Evaluating deep generative models on cognitive tasks: a case study. Discover Artificial Intelligence 3(1), 21 (2023) Liu et al. [2009] Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Grishman, R.: Information extraction. IEEE Intelligent Systems 30(5), 8–15 (2015) Kejriwal [2019] Kejriwal, M.: Information extraction. Domain-Specific Knowledge Graph Construction, 9–31 (2019) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: Information extraction in illicit web domains. In: Proceedings of the 26th International Conference on World Wide Web, pp. 997–1006 (2017) Tang and Kejriwal [2023] Tang, Z., Kejriwal, M.: Evaluating deep generative models on cognitive tasks: a case study. Discover Artificial Intelligence 3(1), 21 (2023) Liu et al. [2009] Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). 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Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M.: Information extraction. Domain-Specific Knowledge Graph Construction, 9–31 (2019) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: Information extraction in illicit web domains. In: Proceedings of the 26th International Conference on World Wide Web, pp. 997–1006 (2017) Tang and Kejriwal [2023] Tang, Z., Kejriwal, M.: Evaluating deep generative models on cognitive tasks: a case study. Discover Artificial Intelligence 3(1), 21 (2023) Liu et al. [2009] Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Szekely, P.: Information extraction in illicit web domains. In: Proceedings of the 26th International Conference on World Wide Web, pp. 997–1006 (2017) Tang and Kejriwal [2023] Tang, Z., Kejriwal, M.: Evaluating deep generative models on cognitive tasks: a case study. Discover Artificial Intelligence 3(1), 21 (2023) Liu et al. [2009] Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Tang, Z., Kejriwal, M.: Evaluating deep generative models on cognitive tasks: a case study. Discover Artificial Intelligence 3(1), 21 (2023) Liu et al. [2009] Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. 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  3. Kejriwal, M., Szekely, P.: Supervised typing of big graphs using semantic embeddings. In: Proceedings of the International Workshop on Semantic Big Data, pp. 1–6 (2017) Kejriwal and Szekely [2017b] Kejriwal, M., Szekely, P.: Scalable generation of type embeddings using the abox. Open Journal of Semantic Web (OJSW) 4(1), 20–34 (2017) Kejriwal et al. [2021] Kejriwal, M., Knoblock, C.A., Szekely, P.: Knowledge Graphs: Fundamentals, Techniques, and Applications. MIT Press, ??? (2021) Schulz and Martínez-Costa [2013] Schulz, S., Martínez-Costa, C.: How ontologies can improve semantic interoperability in health care. In: International Workshop on Process-oriented Information Systems in Healthcare, pp. 1–10 (2013). Springer Grishman [2015] Grishman, R.: Information extraction. IEEE Intelligent Systems 30(5), 8–15 (2015) Kejriwal [2019] Kejriwal, M.: Information extraction. 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Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Szekely, P.: Scalable generation of type embeddings using the abox. Open Journal of Semantic Web (OJSW) 4(1), 20–34 (2017) Kejriwal et al. [2021] Kejriwal, M., Knoblock, C.A., Szekely, P.: Knowledge Graphs: Fundamentals, Techniques, and Applications. MIT Press, ??? (2021) Schulz and Martínez-Costa [2013] Schulz, S., Martínez-Costa, C.: How ontologies can improve semantic interoperability in health care. In: International Workshop on Process-oriented Information Systems in Healthcare, pp. 1–10 (2013). Springer Grishman [2015] Grishman, R.: Information extraction. IEEE Intelligent Systems 30(5), 8–15 (2015) Kejriwal [2019] Kejriwal, M.: Information extraction. Domain-Specific Knowledge Graph Construction, 9–31 (2019) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: Information extraction in illicit web domains. In: Proceedings of the 26th International Conference on World Wide Web, pp. 997–1006 (2017) Tang and Kejriwal [2023] Tang, Z., Kejriwal, M.: Evaluating deep generative models on cognitive tasks: a case study. Discover Artificial Intelligence 3(1), 21 (2023) Liu et al. [2009] Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Knoblock, C.A., Szekely, P.: Knowledge Graphs: Fundamentals, Techniques, and Applications. MIT Press, ??? (2021) Schulz and Martínez-Costa [2013] Schulz, S., Martínez-Costa, C.: How ontologies can improve semantic interoperability in health care. In: International Workshop on Process-oriented Information Systems in Healthcare, pp. 1–10 (2013). Springer Grishman [2015] Grishman, R.: Information extraction. IEEE Intelligent Systems 30(5), 8–15 (2015) Kejriwal [2019] Kejriwal, M.: Information extraction. Domain-Specific Knowledge Graph Construction, 9–31 (2019) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: Information extraction in illicit web domains. In: Proceedings of the 26th International Conference on World Wide Web, pp. 997–1006 (2017) Tang and Kejriwal [2023] Tang, Z., Kejriwal, M.: Evaluating deep generative models on cognitive tasks: a case study. Discover Artificial Intelligence 3(1), 21 (2023) Liu et al. [2009] Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Schulz, S., Martínez-Costa, C.: How ontologies can improve semantic interoperability in health care. In: International Workshop on Process-oriented Information Systems in Healthcare, pp. 1–10 (2013). Springer Grishman [2015] Grishman, R.: Information extraction. IEEE Intelligent Systems 30(5), 8–15 (2015) Kejriwal [2019] Kejriwal, M.: Information extraction. Domain-Specific Knowledge Graph Construction, 9–31 (2019) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: Information extraction in illicit web domains. In: Proceedings of the 26th International Conference on World Wide Web, pp. 997–1006 (2017) Tang and Kejriwal [2023] Tang, Z., Kejriwal, M.: Evaluating deep generative models on cognitive tasks: a case study. Discover Artificial Intelligence 3(1), 21 (2023) Liu et al. [2009] Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Grishman, R.: Information extraction. IEEE Intelligent Systems 30(5), 8–15 (2015) Kejriwal [2019] Kejriwal, M.: Information extraction. Domain-Specific Knowledge Graph Construction, 9–31 (2019) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: Information extraction in illicit web domains. In: Proceedings of the 26th International Conference on World Wide Web, pp. 997–1006 (2017) Tang and Kejriwal [2023] Tang, Z., Kejriwal, M.: Evaluating deep generative models on cognitive tasks: a case study. Discover Artificial Intelligence 3(1), 21 (2023) Liu et al. [2009] Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M.: Information extraction. Domain-Specific Knowledge Graph Construction, 9–31 (2019) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: Information extraction in illicit web domains. In: Proceedings of the 26th International Conference on World Wide Web, pp. 997–1006 (2017) Tang and Kejriwal [2023] Tang, Z., Kejriwal, M.: Evaluating deep generative models on cognitive tasks: a case study. Discover Artificial Intelligence 3(1), 21 (2023) Liu et al. [2009] Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Szekely, P.: Information extraction in illicit web domains. In: Proceedings of the 26th International Conference on World Wide Web, pp. 997–1006 (2017) Tang and Kejriwal [2023] Tang, Z., Kejriwal, M.: Evaluating deep generative models on cognitive tasks: a case study. Discover Artificial Intelligence 3(1), 21 (2023) Liu et al. [2009] Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Tang, Z., Kejriwal, M.: Evaluating deep generative models on cognitive tasks: a case study. Discover Artificial Intelligence 3(1), 21 (2023) Liu et al. [2009] Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022)
  4. Kejriwal, M., Szekely, P.: Scalable generation of type embeddings using the abox. Open Journal of Semantic Web (OJSW) 4(1), 20–34 (2017) Kejriwal et al. [2021] Kejriwal, M., Knoblock, C.A., Szekely, P.: Knowledge Graphs: Fundamentals, Techniques, and Applications. MIT Press, ??? (2021) Schulz and Martínez-Costa [2013] Schulz, S., Martínez-Costa, C.: How ontologies can improve semantic interoperability in health care. In: International Workshop on Process-oriented Information Systems in Healthcare, pp. 1–10 (2013). Springer Grishman [2015] Grishman, R.: Information extraction. IEEE Intelligent Systems 30(5), 8–15 (2015) Kejriwal [2019] Kejriwal, M.: Information extraction. Domain-Specific Knowledge Graph Construction, 9–31 (2019) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: Information extraction in illicit web domains. In: Proceedings of the 26th International Conference on World Wide Web, pp. 997–1006 (2017) Tang and Kejriwal [2023] Tang, Z., Kejriwal, M.: Evaluating deep generative models on cognitive tasks: a case study. Discover Artificial Intelligence 3(1), 21 (2023) Liu et al. [2009] Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Knoblock, C.A., Szekely, P.: Knowledge Graphs: Fundamentals, Techniques, and Applications. MIT Press, ??? (2021) Schulz and Martínez-Costa [2013] Schulz, S., Martínez-Costa, C.: How ontologies can improve semantic interoperability in health care. In: International Workshop on Process-oriented Information Systems in Healthcare, pp. 1–10 (2013). Springer Grishman [2015] Grishman, R.: Information extraction. IEEE Intelligent Systems 30(5), 8–15 (2015) Kejriwal [2019] Kejriwal, M.: Information extraction. Domain-Specific Knowledge Graph Construction, 9–31 (2019) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: Information extraction in illicit web domains. In: Proceedings of the 26th International Conference on World Wide Web, pp. 997–1006 (2017) Tang and Kejriwal [2023] Tang, Z., Kejriwal, M.: Evaluating deep generative models on cognitive tasks: a case study. Discover Artificial Intelligence 3(1), 21 (2023) Liu et al. [2009] Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. 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Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. 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Nature Machine Intelligence 4(4), 318–322 (2022) Schulz, S., Martínez-Costa, C.: How ontologies can improve semantic interoperability in health care. In: International Workshop on Process-oriented Information Systems in Healthcare, pp. 1–10 (2013). Springer Grishman [2015] Grishman, R.: Information extraction. IEEE Intelligent Systems 30(5), 8–15 (2015) Kejriwal [2019] Kejriwal, M.: Information extraction. Domain-Specific Knowledge Graph Construction, 9–31 (2019) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: Information extraction in illicit web domains. In: Proceedings of the 26th International Conference on World Wide Web, pp. 997–1006 (2017) Tang and Kejriwal [2023] Tang, Z., Kejriwal, M.: Evaluating deep generative models on cognitive tasks: a case study. Discover Artificial Intelligence 3(1), 21 (2023) Liu et al. [2009] Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Grishman, R.: Information extraction. IEEE Intelligent Systems 30(5), 8–15 (2015) Kejriwal [2019] Kejriwal, M.: Information extraction. Domain-Specific Knowledge Graph Construction, 9–31 (2019) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: Information extraction in illicit web domains. In: Proceedings of the 26th International Conference on World Wide Web, pp. 997–1006 (2017) Tang and Kejriwal [2023] Tang, Z., Kejriwal, M.: Evaluating deep generative models on cognitive tasks: a case study. Discover Artificial Intelligence 3(1), 21 (2023) Liu et al. [2009] Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M.: Information extraction. Domain-Specific Knowledge Graph Construction, 9–31 (2019) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: Information extraction in illicit web domains. In: Proceedings of the 26th International Conference on World Wide Web, pp. 997–1006 (2017) Tang and Kejriwal [2023] Tang, Z., Kejriwal, M.: Evaluating deep generative models on cognitive tasks: a case study. Discover Artificial Intelligence 3(1), 21 (2023) Liu et al. [2009] Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Szekely, P.: Information extraction in illicit web domains. In: Proceedings of the 26th International Conference on World Wide Web, pp. 997–1006 (2017) Tang and Kejriwal [2023] Tang, Z., Kejriwal, M.: Evaluating deep generative models on cognitive tasks: a case study. Discover Artificial Intelligence 3(1), 21 (2023) Liu et al. [2009] Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Tang, Z., Kejriwal, M.: Evaluating deep generative models on cognitive tasks: a case study. Discover Artificial Intelligence 3(1), 21 (2023) Liu et al. [2009] Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022)
  6. Schulz, S., Martínez-Costa, C.: How ontologies can improve semantic interoperability in health care. In: International Workshop on Process-oriented Information Systems in Healthcare, pp. 1–10 (2013). Springer Grishman [2015] Grishman, R.: Information extraction. IEEE Intelligent Systems 30(5), 8–15 (2015) Kejriwal [2019] Kejriwal, M.: Information extraction. Domain-Specific Knowledge Graph Construction, 9–31 (2019) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: Information extraction in illicit web domains. In: Proceedings of the 26th International Conference on World Wide Web, pp. 997–1006 (2017) Tang and Kejriwal [2023] Tang, Z., Kejriwal, M.: Evaluating deep generative models on cognitive tasks: a case study. Discover Artificial Intelligence 3(1), 21 (2023) Liu et al. [2009] Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Grishman, R.: Information extraction. IEEE Intelligent Systems 30(5), 8–15 (2015) Kejriwal [2019] Kejriwal, M.: Information extraction. Domain-Specific Knowledge Graph Construction, 9–31 (2019) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: Information extraction in illicit web domains. In: Proceedings of the 26th International Conference on World Wide Web, pp. 997–1006 (2017) Tang and Kejriwal [2023] Tang, Z., Kejriwal, M.: Evaluating deep generative models on cognitive tasks: a case study. Discover Artificial Intelligence 3(1), 21 (2023) Liu et al. [2009] Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M.: Information extraction. Domain-Specific Knowledge Graph Construction, 9–31 (2019) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: Information extraction in illicit web domains. In: Proceedings of the 26th International Conference on World Wide Web, pp. 997–1006 (2017) Tang and Kejriwal [2023] Tang, Z., Kejriwal, M.: Evaluating deep generative models on cognitive tasks: a case study. Discover Artificial Intelligence 3(1), 21 (2023) Liu et al. [2009] Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Szekely, P.: Information extraction in illicit web domains. In: Proceedings of the 26th International Conference on World Wide Web, pp. 997–1006 (2017) Tang and Kejriwal [2023] Tang, Z., Kejriwal, M.: Evaluating deep generative models on cognitive tasks: a case study. Discover Artificial Intelligence 3(1), 21 (2023) Liu et al. [2009] Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Tang, Z., Kejriwal, M.: Evaluating deep generative models on cognitive tasks: a case study. Discover Artificial Intelligence 3(1), 21 (2023) Liu et al. [2009] Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. 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Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M.: Information extraction. Domain-Specific Knowledge Graph Construction, 9–31 (2019) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: Information extraction in illicit web domains. In: Proceedings of the 26th International Conference on World Wide Web, pp. 997–1006 (2017) Tang and Kejriwal [2023] Tang, Z., Kejriwal, M.: Evaluating deep generative models on cognitive tasks: a case study. Discover Artificial Intelligence 3(1), 21 (2023) Liu et al. [2009] Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Szekely, P.: Information extraction in illicit web domains. In: Proceedings of the 26th International Conference on World Wide Web, pp. 997–1006 (2017) Tang and Kejriwal [2023] Tang, Z., Kejriwal, M.: Evaluating deep generative models on cognitive tasks: a case study. Discover Artificial Intelligence 3(1), 21 (2023) Liu et al. [2009] Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Tang, Z., Kejriwal, M.: Evaluating deep generative models on cognitive tasks: a case study. Discover Artificial Intelligence 3(1), 21 (2023) Liu et al. [2009] Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022)
  8. Kejriwal, M.: Information extraction. Domain-Specific Knowledge Graph Construction, 9–31 (2019) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: Information extraction in illicit web domains. In: Proceedings of the 26th International Conference on World Wide Web, pp. 997–1006 (2017) Tang and Kejriwal [2023] Tang, Z., Kejriwal, M.: Evaluating deep generative models on cognitive tasks: a case study. Discover Artificial Intelligence 3(1), 21 (2023) Liu et al. [2009] Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Szekely, P.: Information extraction in illicit web domains. In: Proceedings of the 26th International Conference on World Wide Web, pp. 997–1006 (2017) Tang and Kejriwal [2023] Tang, Z., Kejriwal, M.: Evaluating deep generative models on cognitive tasks: a case study. Discover Artificial Intelligence 3(1), 21 (2023) Liu et al. [2009] Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Tang, Z., Kejriwal, M.: Evaluating deep generative models on cognitive tasks: a case study. Discover Artificial Intelligence 3(1), 21 (2023) Liu et al. [2009] Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022)
  9. Kejriwal, M., Szekely, P.: Information extraction in illicit web domains. In: Proceedings of the 26th International Conference on World Wide Web, pp. 997–1006 (2017) Tang and Kejriwal [2023] Tang, Z., Kejriwal, M.: Evaluating deep generative models on cognitive tasks: a case study. Discover Artificial Intelligence 3(1), 21 (2023) Liu et al. [2009] Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Tang, Z., Kejriwal, M.: Evaluating deep generative models on cognitive tasks: a case study. Discover Artificial Intelligence 3(1), 21 (2023) Liu et al. [2009] Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022)
  10. Tang, Z., Kejriwal, M.: Evaluating deep generative models on cognitive tasks: a case study. Discover Artificial Intelligence 3(1), 21 (2023) Liu et al. [2009] Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022)
  11. Liu, T.-Y., et al.: Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3(3), 225–331 (2009) Kejriwal and Szekely [2017] Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022)
  12. Kejriwal, M., Szekely, P.: An investigative search engine for the human trafficking domain. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II 16, pp. 247–262 (2017). Springer Kejriwal [2020] Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022)
  13. Kejriwal, M.: Knowledge graphs and covid-19: opportunities, challenges, and implementation. Harv. Data Sci. Rev 11, 300 (2020) Kejriwal [2022] Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022)
  14. Kejriwal, M.: Knowledge graphs: A practical review of the research landscape. Information 13(4), 161 (2022) Kejriwal [2015] Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022)
  15. Kejriwal, M.: Entity resolution in a big data framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015) Balaji et al. [2016] Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022)
  16. Balaji, J., Javed, F., Kejriwal, M., Min, C., Sander, S., Ozturk, O.: An ensemble blocking scheme for entity resolution of large and sparse datasets. arXiv preprint arXiv:1609.06265 (2016) Kejriwal and Kapoor [2019] Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022)
  17. Kejriwal, M., Kapoor, R.: Network-theoretic information extraction quality assessment in the human trafficking domain. Applied Network Science 4(1), 1–26 (2019) Shen and Kejriwal [2020] Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022)
  18. Shen, K., Kejriwal, M.: A data-driven study of commonsense knowledge using the conceptnet knowledge base. arXiv preprint arXiv:2011.14084 (2020) Kejriwal and Szekely [2019] Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022)
  19. Kejriwal, M., Szekely, P.: mydig: Personalized illicit domain-specific knowledge discovery with no programming. Future Internet 11(3), 59 (2019) Kejriwal et al. [2022] Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022) Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022)
  20. Kejriwal, M., Santos, H., Mulvehill, A.M., McGuinness, D.L.: Designing a strong test for measuring true common-sense reasoning. Nature Machine Intelligence 4(4), 318–322 (2022)

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