Advertisement

A Hybrid Graph Model for Distant Supervision Relation Extraction

  • Shangfu Duan
  • Huan Gao
  • Bing Liu
  • Guilin QiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11503)

Abstract

Distant supervision has advantages of generating training data automatically for relation extraction by aligning triples in Knowledge Graphs with large-scale corpora. Some recent methods attempt to incorporate extra information to enhance the performance of relation extraction. However, there still exist two major limitations. Firstly, these methods are tailored for a specific type of information which is not enough to cover most of the cases. Secondly, the introduced extra information may contain noise. To address these issues, we propose a novel hybrid graph model, which can incorporate heterogeneous background information in a unified framework, such as entity types and human-constructed triples. These various kinds of knowledge can be integrated efficiently even with several missing cases. In addition, we further employ an attention mechanism to identify the most confident information which can alleviate the side effect of noise. Experimental results demonstrate that our model outperforms the state-of-the-art methods significantly in various evaluation metrics.

Keywords

Distant supervision Relation extraction Heterogeneous information Hybrid graph 

Notes

Acknowledgement

This work was supported by National Natural Science Foundation of China Key (U1736204) and National Key R&D Program of China (2018YFC0830200).

References

  1. 1.
    Bollacker, K.D., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of SIGMOD, pp. 1247–1250 (2008)Google Scholar
  2. 2.
    Bordes, A., Usunier, N., García-Durán, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Proceedings of NIPS, pp. 2787–2795 (2013)Google Scholar
  3. 3.
    Craven, M., Kumlien, J.: Constructing biological knowledge bases by extracting information from text sources. In: Proceedings of ISMB, pp. 77–86 (1999)Google Scholar
  4. 4.
    Guu, K., Miller, J., Liang, P.: Traversing knowledge graphs in vector space. In: Proceedings of EMNLP 2015, pp. 318–327 (2015)Google Scholar
  5. 5.
    Han, X., Liu, Z., Sun, M.: Neural knowledge acquisition via mutual attention between knowledge graph and text. In: Proceedings of 8th AAAI and (EAAI-2018), pp. 4832–4839 (2018)Google Scholar
  6. 6.
    Hoffmann, R., Zhang, C., Ling, X., Zettlemoyer, L.S., Weld, D.S.: Knowledge-based weak supervision for information extraction of overlapping relations. In: Proceedings of ACL, pp. 541–550 (2011)Google Scholar
  7. 7.
    Ji, G., Liu, K., He, S., Zhao, J.: Distant supervision for relation extraction with sentence-level attention and entity descriptions. In: Proceedings of AAAI, pp. 3060–3066 (2017)Google Scholar
  8. 8.
    Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: Proceedings of ICLR (2016)Google Scholar
  9. 9.
    Lin, Y., Liu, Z., Luan, H., Sun, M., Rao, S., Liu, S.: Modeling relation paths for representation learning of knowledge bases. In: Proceedings of EMNLP, pp. 705–714 (2015)Google Scholar
  10. 10.
    Lin, Y., Shen, S., Liu, Z., Luan, H., Sun, M.: Neural relation extraction with selective attention over instances. In: Proceedings of ACL, pp. 2124–2133 (2016)Google Scholar
  11. 11.
    Liu, Y., Liu, K., Xu, L., Zhao, J.: Exploring fine-grained entity type constraints for distantly supervised relation extraction. In: Proceedings of COLING, pp. 2107–2116. ACL (2014)Google Scholar
  12. 12.
    McCallum, A., Neelakantan, A., Das, R., Belanger, D.: Chains of reasoning over entities, relations, and text using recurrent neural networks. In: Proceedings of EACL, pp. 132–141 (2017)Google Scholar
  13. 13.
    Mintz, M., Bills, S., Snow, R., Jurafsky, D.: Distant supervision for relation extraction without labeled data. In: Proceedings of ACL, pp. 1003–1011 (2009)Google Scholar
  14. 14.
    Miwa, M., Bansal, M.: End-to-end relation extraction using LSTMs on sequences and tree structures. In: Proceedings of ACL (2016)Google Scholar
  15. 15.
    Neelakantan, A., Roth, B., McCallum, A.: Compositional vector space models for knowledge base completion. In: Proceedings of ACL, pp. 156–166 (2015)Google Scholar
  16. 16.
    Riedel, S., Yao, L., McCallum, A.: Modeling relations and their mentions without labeled text. In: Proceedings of ECML PKDD, pp. 148–163 (2010)CrossRefGoogle Scholar
  17. 17.
    Schlichtkrull, M.S., Kipf, T.N., Bloem, P., van den Berg, R., Titov, I., Welling, M.: Modeling relational data with graph convolutional networks. In: Proceedings of ESWC, pp. 593–607 (2018)CrossRefGoogle Scholar
  18. 18.
    Socher, R., Huval, B., Manning, C.D., Ng, A.Y.: Semantic compositionality through recursive matrix-vector spaces. In: Proceedings of EMNLP-CoNLL, pp. 1201–1211 (2012)Google Scholar
  19. 19.
    Surdeanu, M., Tibshirani, J., Nallapati, R., Manning, C.D.: Multi-instance multi-label learning for relation extraction. In: Proceedings of EMNLP-CoNLL, pp. 455–465 (2012)Google Scholar
  20. 20.
    Toutanova, K., Chen, D., Pantel, P., Poon, H., Choudhury, P., Gamon, M.: Representing text for joint embedding of text and knowledge bases. In: Proceedings of EMNLP 2015, pp. 1499–1509 (2015)Google Scholar
  21. 21.
    Toutanova, K., Lin, V., Yih, W., Poon, H., Quirk, C.: Compositional learning of embeddings for relation paths in knowledge base and text. In: Proceedings of ACL, pp. 1434–1444 (2016)Google Scholar
  22. 22.
    Weston, J., Bordes, A., Yakhnenko, O., Usunier, N.: Connecting language and knowledge bases with embedding models for relation extraction. In: Proceedings of EMNLP, pp. 1366–1371 (2013)Google Scholar
  23. 23.
    Zeng, D., Liu, K., Chen, Y., Zhao, J.: Distant supervision for relation extraction via piecewise convolutional neural networks. In: Proceedings of EMNLP, pp. 1753–1762 (2015)Google Scholar
  24. 24.
    Zeng, D., Liu, K., Lai, S., Zhou, G., Zhao, J.: Relation classification via convolutional deep neural network. In: Proceedings of COLING, pp. 2335–2344 (2014)Google Scholar
  25. 25.
    Zeng, W., Lin, Y., Liu, Z., Sun, M.: Incorporating relation paths in neural relation extraction. In: Proceedings of EMNLP, pp. 1768–1777 (2017)Google Scholar
  26. 26.
    Zheng, H., Li, Z., Wang, S., Yan, Z., Zhou, J.: Aggregating inter-sentence information to enhance relation extraction. In: Proceedings of AAAI, pp. 3108–3115 (2016)Google Scholar
  27. 27.
    Zhou, P., Shi, W., Tian, J., Qi, Z., Li, B., Hao, H., Xu, B.: Attention-based bidirectional long short-term memory networks for relation classification. In: Proceedings of ACL, pp. 207–212 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringSoutheast UniversityNanjingChina

Personalised recommendations