Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
102 tokens/sec
GPT-4o
59 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
6 tokens/sec
GPT-4.1 Pro
50 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

A Survey on Temporal Knowledge Graph: Representation Learning and Applications (2403.04782v1)

Published 2 Mar 2024 in cs.CL and cs.AI

Abstract: Knowledge graphs have garnered significant research attention and are widely used to enhance downstream applications. However, most current studies mainly focus on static knowledge graphs, whose facts do not change with time, and disregard their dynamic evolution over time. As a result, temporal knowledge graphs have attracted more attention because a large amount of structured knowledge exists only within a specific period. Knowledge graph representation learning aims to learn low-dimensional vector embeddings for entities and relations in a knowledge graph. The representation learning of temporal knowledge graphs incorporates time information into the standard knowledge graph framework and can model the dynamics of entities and relations over time. In this paper, we conduct a comprehensive survey of temporal knowledge graph representation learning and its applications. We begin with an introduction to the definitions, datasets, and evaluation metrics for temporal knowledge graph representation learning. Next, we propose a taxonomy based on the core technologies of temporal knowledge graph representation learning methods, and provide an in-depth analysis of different methods in each category. Finally, we present various downstream applications related to the temporal knowledge graphs. In the end, we conclude the paper and have an outlook on the future research directions in this area.

PDF HTML Abstract
Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Bookmark Chat Bubble Oval Streamline Icon: https://streamlinehq.com Chat (Pro)
Definition Search Book Streamline Icon: https://streamlinehq.com
References (88)
  1. Boxe: A box embedding model for knowledge base completion, in: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (Eds.), Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual. URL: https://proceedings.neurips.cc/paper/2020/hash/6dbbe6abe5f14af882ff977fc3f35501-Abstract.html.
  2. Sparse feature factorization for recommender systems with knowledge graphs, in: Proceedings of the 15th ACM Conference on Recommender Systems, Association for Computing Machinery, New York, NY, USA. p. 154–165. URL: https://doi.org/10.1145/3460231.3474243, doi:10.1145/3460231.3474243.
  3. TuckER: Tensor factorization for knowledge graph completion, in: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Association for Computational Linguistics, Stroudsburg, PA, USA. pp. 5185–5194. URL: https://aclanthology.org/D19-1522, doi:10.18653/v1/D19-1522.
  4. A markovian decision process. Indiana Univ. Math. J. 6, 679–684. URL: http://www.iumj.indiana.edu/IUMJ/FULLTEXT/1957/6/56038.
  5. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901.
  6. Temporal knowledge graph completion: A survey. CoRR abs/2201.08236. URL: https://arxiv.org/abs/2201.08236, arXiv:2201.08236.
  7. A simple temporal information matching mechanism for entity alignment between temporal knowledge graphs, in: Proceedings of the 29th International Conference on Computational Linguistics, International Committee on Computational Linguistics, Gyeongju, Republic of Korea. pp. 2075–2086. URL: https://aclanthology.org/2022.coling-1.181.
  8. Zero-shot and few-shot learning with knowledge graphs: A comprehensive survey. Proceedings of the IEEE , 1–33doi:10.1109/JPROC.2023.3279374.
  9. RotateQVS: Representing temporal information as rotations in quaternion vector space for temporal knowledge graph completion, in: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, Stroudsburg, PA, USA. pp. 5843–5857. URL: https://aclanthology.org/2022.acl-long.402, doi:10.18653/v1/2022.acl-long.402.
  10. Meta-learning based knowledge extrapolation for temporal knowledge graph, in: Proceedings of the ACM Web Conference 2023, Association for Computing Machinery, New York, NY, USA. p. 2433–2443. URL: https://doi.org/10.1145/3543507.3583279, doi:10.1145/3543507.3583279.
  11. Learning phrase representations using RNN encoder-decoder for statistical machine translation, in: Moschitti, A., Pang, B., Daelemans, W. (Eds.), Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, October 25-29, 2014, Doha, Qatar, A meeting of SIGDAT, a Special Interest Group of the ACL, Association for Computational Linguistics, Stroudsburg, PA, USA. pp. 1724–1734. URL: https://doi.org/10.3115/v1/d14-1179, doi:10.3115/v1/d14-1179.
  12. An Introduction to the Theory of Point Processes. Volume II: General Theory and Structure. Springer, Berlin, GER. URL: https://link.springer.com/book/10.1007/978-0-387-49835-5.
  13. HyTE: Hyperplane-based temporally aware knowledge graph embedding, in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Stroudsburg, PA, USA. pp. 2001–2011. URL: https://aclanthology.org/D18-1225, doi:10.18653/v1/D18-1225.
  14. Dynamic knowledge graph based multi-event forecasting, in: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery; Data Mining, Association for Computing Machinery, New York, NY, USA. p. 1585–1595. URL: https://doi.org/10.1145/3394486.3403209, doi:10.1145/3394486.3403209.
  15. BERT: pre-training of deep bidirectional transformers for language understanding, in: Burstein, J., Doran, C., Solorio, T. (Eds.), Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume 1 (Long and Short Papers), Association for Computational Linguistics, Stroudsburg, PA, USA. pp. 4171–4186. URL: https://doi.org/10.18653/v1/n19-1423, doi:10.18653/v1/n19-1423.
  16. Utilizing knowledge graphs for text-centric information retrieval, in: The 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, Association for Computing Machinery, New York, NY, USA. p. 1387–1390. URL: https://doi.org/10.1145/3209978.3210187, doi:10.1145/3209978.3210187.
  17. Zero-shot relational learning on temporal knowledge graphs with large language models. arXiv preprint arXiv:2311.10112 .
  18. TempCaps: A capsule network-based embedding model for temporal knowledge graph completion, in: Proceedings of the Sixth Workshop on Structured Prediction for NLP, Association for Computational Linguistics, Stroudsburg, PA, USA. pp. 22–31. URL: https://aclanthology.org/2022.spnlp-1.3, doi:10.18653/v1/2022.spnlp-1.3.
  19. Learning sequence encoders for temporal knowledge graph completion, in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Stroudsburg, PA, USA. pp. 4816–4821. URL: https://aclanthology.org/D18-1516, doi:10.18653/v1/D18-1516.
  20. Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmospheric environment 32, 2627–2636. URL: https://doi.org/10.1016/S1352-2310(97)00447-0.
  21. Diachronic embedding for temporal knowledge graph completion, in: The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, New York, NY, USA, February 7-12, 2020, AAAI Press, Palo Alto, CA, USA. pp. 3988–3995. URL: https://ojs.aaai.org/index.php/AAAI/article/view/5815.
  22. Temporal-relational matching network for few-shot temporal knowledge graph completion, in: Database Systems for Advanced Applications: 28th International Conference, DASFAA 2023, Tianjin, China, April 17–20, 2023, Proceedings, Part II, Springer-Verlag, Berlin, Heidelberg. p. 768–783. URL: https://doi.org/10.1007/978-3-031-30672-3_52, doi:10.1007/978-3-031-30672-3_52.
  23. Explainable subgraph reasoning for forecasting on temporal knowledge graphs, in: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021, OpenReview.net, Austria. URL: https://openreview.net/forum?id=pGIHq1m7PU.
  24. Learning neural ordinary equations for forecasting future links on temporal knowledge graphs, in: Moens, M., Huang, X., Specia, L., Yih, S.W. (Eds.), Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 7-11 November, 2021, Association for Computational Linguistics. pp. 8352–8364. URL: https://doi.org/10.18653/v1/2021.emnlp-main.658, doi:10.18653/v1/2021.emnlp-main.658.
  25. Ecola: Enhanced temporal knowledge embeddings with contextualized language representations. arXiv:2203.09590.
  26. Dyernie: Dynamic evolution of riemannian manifold embeddings for temporal knowledge graph completion. arXiv:2011.03984.
  27. Graph hawkes neural network for forecasting on temporal knowledge graphs, in: Das, D., Hajishirzi, H., McCallum, A., Singh, S. (Eds.), Conference on Automated Knowledge Base Construction, AKBC 2020, Virtual, June 22-24, 2020, OpenReview.net, Virtual. URL: https://doi.org/10.24432/C50018, doi:10.24432/C50018.
  28. Spectra of some self-exciting and mutually exciting point processes. Biometrika 58, 83–90. URL: https://doi.org/10.1093/biomet/58.1.83.
  29. Improving multi-hop knowledge base question answering by learning intermediate supervision signals, in: Proceedings of the 14th ACM International Conference on Web Search and Data Mining, Association for Computing Machinery, New York, NY, USA. p. 553–561. URL: https://doi.org/10.1145/3437963.3441753, doi:10.1145/3437963.3441753.
  30. The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6, 164–189. URL: https://doi.org/10.1002/sapm192761164, doi:10.1002/sapm192761164.
  31. Long short-term memory. Neural Comput. 9, 1735–1780. URL: https://doi.org/10.1162/neco.1997.9.8.1735, doi:10.1162/neco.1997.9.8.1735.
  32. Yago2: Exploring and querying world knowledge in time, space, context, and many languages, in: Proceedings of the 20th International Conference Companion on World Wide Web, Association for Computing Machinery, New York, NY, USA. p. 229–232. URL: https://doi.org/10.1145/1963192.1963296, doi:10.1145/1963192.1963296.
  33. A survey on knowledge graphs: Representation, acquisition, and applications. IEEE transactions on neural networks and learning systems 33, 494–514. URL: https://doi.org/10.1109/TNNLS.2021.3070843, doi:10.1109/TNNLS.2021.3070843.
  34. Recurrent event network: Autoregressive structure inferenceover temporal knowledge graphs, in: Webber, B., Cohn, T., He, Y., Liu, Y. (Eds.), Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16-20, 2020, Association for Computational Linguistics, Stroudsburg, PA, USA. pp. 6669–6683. URL: https://doi.org/10.18653/v1/2020.emnlp-main.541, doi:10.18653/v1/2020.emnlp-main.541.
  35. Reinforcement learning: A survey. J. Artif. Intell. Res. 4, 237–285. URL: https://doi.org/10.1613/jair.301, doi:10.1613/jair.301.
  36. Integrative few-shot learning for classification and segmentation, in: IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA, June 18-24, 2022, IEEE. pp. 9969–9980. URL: https://doi.org/10.1109/CVPR52688.2022.00974, doi:10.1109/CVPR52688.2022.00974.
  37. Simple embedding for link prediction in knowledge graphs, in: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (Eds.), Advances in Neural Information Processing Systems, Curran Associates, Inc., New York, NY, USA. URL: https://proceedings.neurips.cc/paper/2018/file/b2ab001909a8a6f04b51920306046ce5-Paper.pdf.
  38. Semi-supervised classification with graph convolutional networks, in: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings, OpenReview.net, Toulon, France. URL: https://openreview.net/forum?id=SJU4ayYgl.
  39. Tensor decompositions for temporal knowledge base completion, in: 8th International Conference on Learning Representations, ICLR 2020, OpenReview.net, Addis Ababa, Ethiopia. URL: https://openreview.net/forum?id=rke2P1BFwS.
  40. Deriving validity time in knowledge graph, in: Companion Proceedings of the The Web Conference 2018, International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE. p. 1771–1776. URL: https://doi.org/10.1145/3184558.3191639, doi:10.1145/3184558.3191639.
  41. Temporal knowledge graph forecasting without knowledge using in-context learning. arXiv preprint arXiv:2305.10613 .
  42. Gdelt: Global data on events, location, and tone, 1979–2012, in: ISA annual convention, Citeseer. Citeseer, State College, PA, USA. pp. 1–49. URL: http://data.gdeltproject.org/documentation/ISA.2013.GDELT.pdf.
  43. Tirgn: Time-guided recurrent graph network with local-global historical patterns for temporal knowledge graph reasoning, in: Raedt, L.D. (Ed.), Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI 2022, Vienna, Austria, 23-29 July 2022, ijcai.org, San Francisco, CA, USA. pp. 2152–2158. URL: https://doi.org/10.24963/ijcai.2022/299, doi:10.24963/ijcai.2022/299.
  44. Complex evolutional pattern learning for temporal knowledge graph reasoning, in: Muresan, S., Nakov, P., Villavicencio, A. (Eds.), Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), ACL 2022, Dublin, Ireland, May 22-27, 2022, Association for Computational Linguistics, Stroudsburg, PA, USA. pp. 290–296. URL: https://doi.org/10.18653/v1/2022.acl-short.32, doi:10.18653/v1/2022.acl-short.32.
  45. Search from history and reason for future: Two-stage reasoning on temporal knowledge graphs, in: Zong, C., Xia, F., Li, W., Navigli, R. (Eds.), Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL/IJCNLP 2021, (Volume 1: Long Papers), Virtual Event, August 1-6, 2021, Association for Computational Linguistics, Stroudsburg, PA, USA. pp. 4732–4743. URL: https://doi.org/10.18653/v1/2021.acl-long.365, doi:10.18653/v1/2021.acl-long.365.
  46. Temporal knowledge graph reasoning based on evolutional representation learning, in: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Association for Computing Machinery, New York, NY, USA. p. 408–417. URL: https://doi.org/10.1145/3404835.3462963, doi:10.1145/3404835.3462963.
  47. Gentkg: Generative forecasting on temporal knowledge graph. arXiv preprint arXiv:2310.07793 .
  48. Tensor decomposition-based temporal knowledge graph embedding, in: 32nd IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2020, Baltimore, MD, USA, November 9-11, 2020, IEEE, Baltimore, MD, USA. pp. 969–975. URL: https://doi.org/10.1109/ICTAI50040.2020.00151, doi:10.1109/ICTAI50040.2020.00151.
  49. Unseen object few-shot semantic segmentation for robotic grasping. IEEE Robotics Autom. Lett. 8, 320–327. URL: https://doi.org/10.1109/LRA.2022.3226073, doi:10.1109/LRA.2022.3226073.
  50. Chain of history: Learning and forecasting with llms for temporal knowledge graph completion. arXiv preprint arXiv:2401.06072 .
  51. The neural hawkes process: A neurally self-modulating multivariate point process, in: Guyon, I., von Luxburg, U., Bengio, S., Wallach, H.M., Fergus, R., Vishwanathan, S.V.N., Garnett, R. (Eds.), Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, Curran Associates, Inc., New York, NY, USA. pp. 6754–6764. URL: https://proceedings.neurips.cc/paper/2017/hash/6463c88460bd63bbe256e495c63aa40b-Abstract.html.
  52. Temporal knowledge graph completion using box embeddings, in: Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022, Thirty-Fourth Conference on Innovative Applications of Artificial Intelligence, IAAI 2022, The Twelveth Symposium on Educational Advances in Artificial Intelligence, EAAI 2022 Virtual Event, February 22 - March 1, 2022, AAAI Press. pp. 7779–7787. URL: https://ojs.aaai.org/index.php/AAAI/article/view/20746.
  53. Survey on temporal knowledge graph, in: Sixth IEEE International Conference on Data Science in Cyberspace, DSC 2021, Shenzhen, China, October 9-11, 2021, IEEE, Berkeley,CA, USA. pp. 294–300. URL: https://doi.org/10.1109/DSC53577.2021.00047, doi:10.1109/DSC53577.2021.00047.
  54. A capsule network-based embedding model for knowledge graph completion and search personalization, in: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Association for Computational Linguistics, Stroudsburg, PA, USA. pp. 2180–2189. URL: https://aclanthology.org/N19-1226, doi:10.18653/v1/N19-1226.
  55. Crisis early warning and decision support: Contemporary approaches and thoughts on future research. International studies review 12, 87–104. URL: https://doi.org/10.1111/j.1468-2486.2009.00914.x.
  56. OpenAI, 2023. Gpt-4 technical report. arXiv:2303.08774.
  57. Evokg: Jointly modeling event time and network structure for reasoning over temporal knowledge graphs, in: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, Association for Computing Machinery, New York, NY, USA. p. 794–803. URL: https://doi.org/10.1145/3488560.3498451, doi:10.1145/3488560.3498451.
  58. Chronor: Rotation based temporal knowledge graph embedding, in: Proceedings of the AAAI Conference on Artificial Intelligence, AAAI2021, AAAI Press, Palo Alto, CA, USA. pp. 6471–6479. URL: https://ojs.aaai.org/index.php/AAAI/article/view/16802, doi:10.1609/aaai.v35i7.16802.
  59. Question answering over temporal knowledge graphs, in: Zong, C., Xia, F., Li, W., Navigli, R. (Eds.), Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL/IJCNLP 2021, (Volume 1: Long Papers), Virtual Event, August 1-6, 2021, Association for Computational Linguistics, Stroudsburg, PA, USA. pp. 6663–6676. URL: https://doi.org/10.18653/v1/2021.acl-long.520, doi:10.18653/v1/2021.acl-long.520.
  60. The graph neural network model. IEEE Transactions on Neural Networks 20, 61–80. URL: https://ieeexplore.ieee.org/document/4700287, doi:10.1109/TNN.2008.2005605.
  61. Modeling relational data with graph convolutional networks, in: Gangemi, A., Navigli, R., Vidal, M., Hitzler, P., Troncy, R., Hollink, L., Tordai, A., Alam, M. (Eds.), The Semantic Web - 15th International Conference, ESWC 2018, Heraklion, Crete, Greece, June 3-7, 2018, Proceedings, Springer, Berlin, GER. pp. 593–607. URL: https://doi.org/10.1007/978-3-319-93417-4_38, doi:10.1007/978-3-319-93417-4_38.
  62. End-to-end structure-aware convolutional networks for knowledge base completion, in: The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, January 27 - February 1, 2019, AAAI Press, Palo Alto, CA, USA. pp. 3060–3067. URL: https://doi.org/10.1609/aaai.v33i01.33013060, doi:10.1609/aaai.v33i01.33013060.
  63. Improving time sensitivity for question answering over temporal knowledge graphs, in: Muresan, S., Nakov, P., Villavicencio, A. (Eds.), Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2022, Dublin, Ireland, May 22-27, 2022, Association for Computational Linguistics, Stroudsburg, PA, USA. pp. 8017–8026. URL: https://doi.org/10.18653/v1/2022.acl-long.552, doi:10.18653/v1/2022.acl-long.552.
  64. Tucker decomposition-based temporal knowledge graph completion. Knowl. Based Syst. 238, 107841. URL: https://doi.org/10.1016/j.knosys.2021.107841, doi:10.1016/j.knosys.2021.107841.
  65. Timetraveler: Reinforcement learning for temporal knowledge graph forecasting, in: Moens, M., Huang, X., Specia, L., Yih, S.W. (Eds.), Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 7-11 November, 2021, Association for Computational Linguistics, Stroudsburg, PA, USA. pp. 8306–8319. URL: https://doi.org/10.18653/v1/2021.emnlp-main.655, doi:10.18653/v1/2021.emnlp-main.655.
  66. Rotate: Knowledge graph embedding by relational rotation in complex space, in: 7th International Conference on Learning Representations, ICLR 2019, OpenReview.net, New Orleans, LA, USA. URL: https://openreview.net/forum?id=HkgEQnRqYQ.
  67. Know-evolve: Deep temporal reasoning for dynamic knowledge graphs, in: Precup, D., Teh, Y.W. (Eds.), Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017, PMLR, Cambridge, MA, USA. pp. 3462–3471. URL: http://proceedings.mlr.press/v70/trivedi17a.html.
  68. Complex embeddings for simple link prediction, in: Proceedings of The 33rd International conference on machine learning, PMLR, New York, NY, USA. pp. 2071–2080. URL: https://proceedings.mlr.press/v48/trouillon16.html.
  69. Some mathematical notes on three-mode factor analysis. Psychometrika 31, 279–311. URL: http://hdl.handle.net/10.1007/BF02289464, doi:10.1007/BF02289464.
  70. Composition-based multi-relational graph convolutional networks, in: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020, OpenReview.net, Addis Ababa, Ethiopia. URL: https://openreview.net/forum?id=BylA_C4tPr.
  71. Attention is all you need, in: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (Eds.), Advances in Neural Information Processing Systems, Curran Associates, Inc. URL: https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf.
  72. Graph attention networks, in: 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings, OpenReview.net, Vancouver, BC, Canada. URL: https://openreview.net/forum?id=rJXMpikCZ.
  73. Wikidata: A free collaborative knowledgebase. Commun. ACM 57, 78–85. URL: https://doi.org/10.1145/2629489, doi:10.1145/2629489.
  74. Dynamic embedding graph attention networks for temporal knowledge graph completion, in: Memmi, G., Yang, B., Kong, L., Zhang, T., Qiu, M. (Eds.), Knowledge Science, Engineering and Management, Springer International Publishing, Cham. pp. 722–734. URL: https://doi.org/10.1007/978-3-031-10983-6_55.
  75. Learning to sample and aggregate: Few-shot reasoning over temporal knowledge graphs, in: Oh, A.H., Agarwal, A., Belgrave, D., Cho, K. (Eds.), Advances in Neural Information Processing Systems. URL: https://openreview.net/forum?id=1LmgISIDZJ.
  76. Generalizing from a few examples: A survey on few-shot learning. ACM Comput. Surv. 53. URL: https://doi.org/10.1145/3386252, doi:10.1145/3386252.
  77. Low-shot learning in natural language processing, in: 2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI), pp. 185–189. doi:10.1109/CogMI50398.2020.00031.
  78. MetaTKG: Learning evolutionary meta-knowledge for temporal knowledge graph reasoning, in: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Abu Dhabi, United Arab Emirates. pp. 7230–7240. URL: https://aclanthology.org/2022.emnlp-main.487.
  79. TeRo: A time-aware knowledge graph embedding via temporal rotation, in: Proceedings of the 28th International Conference on Computational Linguistics, Association for Computational Linguistics, Stroudsburg, PA, USA. pp. 1583–1593. URL: https://aclanthology.org/2020.coling-main.139, doi:10.18653/v1/2020.coling-main.139.
  80. Geometric algebra based embeddings for static and temporal knowledge graph completion. IEEE Transactions on Knowledge and Data Engineering 35, 4838–4851. doi:10.1109/TKDE.2022.3151435.
  81. Time-aware graph neural network for entity alignment between temporal knowledge graphs, in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Stroudsburg, PA, USA. pp. 8999–9010. URL: https://aclanthology.org/2021.emnlp-main.709, doi:10.18653/v1/2021.emnlp-main.709.
  82. Time-aware entity alignment using temporal relational attention, in: Proceedings of the ACM Web Conference 2022, Association for Computing Machinery, New York, NY, USA. p. 788–797. URL: https://doi.org/10.1145/3485447.3511922, doi:10.1145/3485447.3511922.
  83. Along the time: Timeline-traced embedding for temporal knowledge graph completion, in: Proceedings of the 31st ACM International Conference on Information & Knowledge Management, Association for Computing Machinery, New York, NY, USA. p. 2529–2538. URL: https://doi.org/10.1145/3511808.3557233, doi:10.1145/3511808.3557233.
  84. Learning latent relations for temporal knowledge graph reasoning, in: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 12617–12631.
  85. Biqcap: A biquaternion and capsule network-based embedding model for temporal knowledge graph completion, in: Wang, X., Sapino, M.L., Han, W., Abbadi, A.E., Dobbie, G., Feng, Z., Shao, Y., Yin, H. (Eds.), Database Systems for Advanced Applications - 28th International Conference, DASFAA 2023, Tianjin, China, April 17-20, 2023, Proceedings, Part II, Springer. pp. 673–688. URL: https://doi.org/10.1007/978-3-031-30672-3_45, doi:10.1007/978-3-031-30672-3_45.
  86. Ducape: Dual quaternion and capsule network–based temporal knowledge graph embedding. ACM Trans. Knowl. Discov. Data 17. URL: https://doi.org/10.1145/3589644, doi:10.1145/3589644.
  87. A survey of large language models. arXiv:2303.18223.
  88. Learning from history: Modeling temporal knowledge graphs with sequential copy-generation networks, in: Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 2021, AAAI Press, Palo Alto, CA, USA. pp. 4732–4740. URL: https://ojs.aaai.org/index.php/AAAI/article/view/16604.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (6)
  1. Li Cai (33 papers)
  2. Xin Mao (48 papers)
  3. Yuhao Zhou (78 papers)
  4. Zhaoguang Long (2 papers)
  5. Changxu Wu (4 papers)
  6. Man Lan (26 papers)
Citations (7)
View on Semantic Scholar
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets