Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
126 tokens/sec
GPT-4o
28 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

FALCON: Scalable Reasoning over Inconsistent ALC Ontologies (2208.07628v5)

Published 16 Aug 2022 in cs.AI and cs.LO

Abstract: Ontologies are one of the richest sources of knowledge. Real-world ontologies often contain thousands of axioms and are often human-made. Hence, they may contain inconsistency and incomplete information which may impair classical reasoners to compute entailments that are considered as useful. To overcome these two challenges, we propose FALCON, a Fuzzy Ontology Neural reasoner to approximate reasoning over ALC ontologies. We provide an approximate technique for the model generation step in classical ALC reasoners. Our approximation is not guaranteed to construct exact logical models, but can approximate arbitrary models, which is notably faster for some large ontologies. Moreover, by sampling multiple approximate logical models, our technique supports approximate entailment also over inconsistent ontologies. Theoretical results show that more models generated lead to closer, i.e., faithful approximation of entailment over ALC entailments. Experimental results show that FALCON enables approximate reasoning and reasoning in the presence of inconsistency. Our experiments further demonstrate how ontologies can improve knowledge base completion in biomedicine by incorporating knowledge expressed in ALC.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (36)
  1. The description logic handbook: Theory, implementation and applications. Cambridge university press.
  2. Logic tensor networks. Artificial Intelligence, 303: 103649.
  3. Bienvenu, M. 2020. A short survey on inconsistency handling in ontology-mediated query answering. KI-Künstliche Intelligenz, 34(4): 443–451.
  4. Translating embeddings for modeling multi-relational data. Advances in neural information processing systems, 26.
  5. Approximate entailment. In Ardizzone, E.; Gaglio, S.; and Sorbello, F., eds., Trends in Artificial Intelligence, 68–77. Berlin, Heidelberg: Springer Berlin Heidelberg. ISBN 978-3-540-46443-3.
  6. A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network. In Proceedings of NAACL-HLT, 327–333.
  7. Ontology change: classification and survey. The Knowledge Engineering Review, 23(2): 117–152.
  8. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the thirteenth international conference on artificial intelligence and statistics, 249–256. JMLR Workshop and Conference Proceedings.
  9. Embedding logical queries on knowledge graphs. Advances in neural information processing systems, 31.
  10. Multilayer feedforward networks are universal approximators. Neural networks, 2(5): 359–366.
  11. Applied logistic regression, volume 398. John Wiley & Sons.
  12. Efficient Paraconsistent Reasoning with Ontologies and Rules. In Proceedings of the 24th International Conference on Artificial Intelligence, IJCAI’15, 3098–3105. AAAI Press. ISBN 9781577357384.
  13. The incredible elk. Journal of automated reasoning, 53(1): 1–61.
  14. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
  15. EL Embeddings: Geometric Construction of Models for the Description Logic EL++. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization.
  16. The Human Phenotype Ontology in 2021. Nucleic Acids Research, 49(D1): D1207–D1217.
  17. Cone Semantics for Logics with Negation. In Bessiere, C., ed., Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI-20, 1820–1826. International Joint Conferences on Artificial Intelligence Organization. Main track.
  18. Logic embeddings for complex query answering. arXiv preprint arXiv:2103.00418.
  19. EmEL++: Embeddings for EL++ description logic. In AAAI Spring Symposium on Combining Machine Learning and Knowledge Engineering. CEUR-WS.
  20. Hypertableau Reasoning for Description Logics. Journal of Artificial Intelligence Research, 36: 165–228.
  21. The BioGRID database: A comprehensive biomedical resource of curated protein, genetic, and chemical interactions. Protein Science, 30(1): 187–200.
  22. Description Logic EL++ Embeddings with Intersectional Closure. arXiv preprint arXiv:2202.14018.
  23. Query2box: Reasoning Over Knowledge Graphs In Vector Space Using Box Embeddings. In International Conference on Learning Representations (ICLR).
  24. Beta embeddings for multi-hop logical reasoning in knowledge graphs. Advances in Neural Information Processing Systems, 33: 19716–19726.
  25. BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618.
  26. Non-Standard Reasoning Services for the Debugging of Description Logic Terminologies. In Proceedings of the 18th International Joint Conference on Artificial Intelligence, IJCAI’03, 355–360. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.
  27. Onto2vec: joint vector-based representation of biological entities and their ontology-based annotations. Bioinformatics, 34(13): i52–i60.
  28. OPA2Vec: combining formal and informal content of biomedical ontologies to improve similarity-based prediction. Bioinformatics, 35(12): 2133–2140.
  29. The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nature biotechnology, 25(11): 1251–1255.
  30. Faithful embeddings for knowledge base queries. Advances in Neural Information Processing Systems, 33: 22505–22516.
  31. Positive-Unlabeled Learning with Adversarial Data Augmentation for Knowledge Graph Completion. arXiv preprint arXiv:2205.00904.
  32. FaCT++ Description Logic Reasoner: System Description. In Furbach, U.; and Shankar, N., eds., Automated Reasoning, 292–297. Berlin, Heidelberg: Springer Berlin Heidelberg. ISBN 978-3-540-37188-5.
  33. Analyzing Differentiable Fuzzy Logic Operators. Artificial Intelligence, 302: 103602.
  34. Neural-Symbolic Reasoning Under Open-World and Closed-World Assumptions. In CEUR Workshop Proceedings, volume 3121. CEUR.
  35. Faithful Embeddings for EL++ Knowledge Bases. In Proceedings of the 21st International Semantic Web Conference (ISWC2022), 1–18.
  36. Embedding Entities and Relations for Learning and Inference in Knowledge Bases. In Proceedings of the International Conference on Learning Representations (ICLR) 2015.
Citations (4)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now