Abstract
Over the past few years, many large Knowledge Bases (KBs) have been constructed through relation extraction technology but they are still often incomplete. As a supplement to training a more powerful extractor, Knowledge Base Completion which aims at learning new facts based on existing ones has recently attracted much attention. Most of the existing methods, however, are only utilizing the explicit facts in a single KB. By analyzing the data, we find that some implicit information should also been captured for a more comprehensive consideration during completion process. These information include the intrinsic properties of KBs (e.g. relational constraints) and potential synergies between various KBs (i.e. semantic similarity). For the former, we distinguish the missing data by using relational constraints to reduce the data sparsity. For the later, we incorporate two semantical regularizations into the learning model to encode the semantic similarity. Experimental results show that our approach is better than the methods that consider only explicit facts or only a single knowledge base, and achieves significant accuracy improvements in binary relation prediction.
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References
Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.G.: DBpedia: a nucleus for a web of open data. In: Aberer, K., et al. (eds.) ASWC 2007 and ISWC 2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007)
Berant, J., Dagan, I., Goldberger, J.: Global learning of typed entailment rules. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 610–619. Association for Computational Linguistics (2011)
Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the 2008 ACM SIGMOD international conference on Management of data, pp. 1247–1250. ACM (2008)
Bordes, A., Glorot, X., Weston, J., Bengio, Y.: A semantic matching energy function for learning with multi-relational data. Mach. Learn. 94(2), 233–259 (2014)
Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Advances in Neural Information Processing Systems, pp. 2787–2795 (2013)
Carlson, A., Betteridge, J., Wang, R.C., Hruschka Jr., E.R., Mitchell, T.M.: Coupled semi-supervised learning for information extraction. In: Proceedings of the third ACM International Conference on Web Search and Data Mining, pp. 101–110. ACM (2010)
Chang, K.W., Yih, W.t., Yang, B., Meek, C.: Typed tensor decomposition of knowledge bases for relation extraction. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1568–1579 (2014)
Gardner, M., Talukdar, P.P., Kisiel, B., Mitchell, T.M.: Improving learning and inference in a large knowledge-base using latent syntactic cues. In: EMNLP, pp. 833–838 (2013)
Hoffmann, R., Zhang, C., Ling, X., Zettlemoyer, L., Weld, D.S.: Knowledge-based weak supervision for information extraction of overlapping relations. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol.1, pp. 541–550. Association for Computational Linguistics (2011)
Jenatton, R., Roux, N.L., Bordes, A., Obozinski, G.R.: A latent factor model for highly multi-relational data. In: Advances in Neural Information Processing Systems. pp. 3167–3175 (2012)
Kemp, C., Tenenbaum, J.B., Griffiths, T.L., Yamada, T., Ueda, N.: Learning systems of concepts with an infinite relational model. In: AAAI, vol. 3, p. 5 (2006)
Lao, N., Mitchell, T., Cohen, W.W.: Random walk inference and learning in a large scale knowledge base. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 529–539. Association for Computational Linguistics (2011)
Lao, N., Subramanya, A., Pereira, F., Cohen, W.W.: Reading the web with learned syntactic-semantic inference rules. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 1017–1026. Association for Computational Linguistics (2012)
Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of AAAI (2015)
Min, B., Grishman, R., Wan, L., Wang, C., Gondek, D.: Distant supervision for relation extraction with an incomplete knowledge base. In: HLT-NAACL, pp. 777–782 (2013)
Nickel, M., Tresp, V., Kriegel, H.P.: A three-way model for collective learning on multi-relational data. In: Proceedings of the 28th International Conference on Machine Learning (ICML 2011), pp. 809–816 (2011)
Nickel, M., Tresp, V., Kriegel, H.P.: Factorizing yago: scalable machine learning for linked data. In: Proceedings of the 21st International Conference on World Wide Web, pp. 271–280. ACM (2012)
Wang, Q., Bin Wang, L.G.: Knowledge base completion using embeddings and rules. In: AAAI (2015, pages to appear)
Riedel, S., Yao, L., Marlin, B.M., McCallum, A.: Relation extraction with matrix factorization and universal schemas. In: Joint Human Language Technology Conference/Annual Meeting of the North American Chapter of the Association for Computational Linguistics (HLT-NAACL 2013), June 2013
Rocktäschel, T., Singh, S., Riedel, S.: Injecting logical background knowledge into embeddings for relation extraction. In: Proceedings of the 2015 Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics (2015)
Socher, R., Chen, D., Manning, C.D., Ng, A.: Reasoning with neural tensor networks for knowledge base completion. In: Advances in Neural Information Processing Systems, pp. 926–934 (2013)
Suchanek, F.M., Abiteboul, S., Senellart, P.: Paris: Probabilistic alignment of relations, instances, and schema. Proc. VLDB Endow. 5(3), 157–168 (2011)
Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge. In: Proceedings of the 16th International Conference on World Wide Web, pp. 697–706. ACM (2007)
Sutskever, I., Tenenbaum, J.B., Salakhutdinov, R.R.: Modelling relational data using bayesian clustered tensor factorization. In: Advances in Neural Information Processing Systems, pp. 1821–1828 (2009)
Weston, J., Bordes, A., Yakhnenko, O., Usunier, N.: Connecting language and knowledge bases with embedding models for relation extraction (2013). arXiv preprint arXiv:1307.7973
Yao, X., Van Durme, B.: Information extraction over structured data: question answering with freebase. In: Proceedings of ACL (2014)
Zou, L., Huang, R., Wang, H., Yu, J.X., He, W., Zhao, D.: Natural language question answering over RDF: a graph data driven approach. In: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, pp. 313–324. ACM (2014)
Acknowledgments
This work was supported by the National High Technology R&D Program of China (Grant No.2014AA015102, 2015AA015403), National Natural Science Foundation of China (Grant No.61272344, 61202233, 61370055) and the joint project with IBM Research. Any correspondence please refer to Wenqiang He.
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He, W., Feng, Y., Zhao, D. (2016). Improving Knowledge Base Completion by Incorporating Implicit Information. In: Qi, G., Kozaki, K., Pan, J., Yu, S. (eds) Semantic Technology. JIST 2015. Lecture Notes in Computer Science(), vol 9544. Springer, Cham. https://doi.org/10.1007/978-3-319-31676-5_10
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DOI: https://doi.org/10.1007/978-3-319-31676-5_10
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