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Knowledge Graph Embedding with Logical Consistency

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Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data (CCL 2018, NLP-NABD 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11221))

Abstract

Existing methods for knowledge graph embedding do not ensure the high-rank triples predicted by themselves to be as consistent as possible with the logical background which is made up of a knowledge graph and a logical theory. Users must take great effort to filter consistent triples before adding new triples to the knowledge graph. To alleviate users’ burden, we propose an approach to enhancing existing embedding-based methods to encode logical consistency into the learnt distributed representation for the knowledge graph, enforcing high-rank new triples as consistent as possible. To evaluate this approach, four knowledge graphs with logical theories are constructed from the four great classical masterpieces of Chinese literature. Experimental results on these datasets show that our approach is able to guarantee high-rank triples as consistent as possible while preserving a comparable performance as baseline methods in link prediction and triple classification.

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Notes

  1. 1.

    https://www.rapidminer.com/.

  2. 2.

    https://protege.stanford.edu/.

  3. 3.

    http://www.cs.ox.ac.uk/projects/requiem/.

  4. 4.

    http://www.openkg.cn/tool/kge-wlc.

References

  1. Bordes, A., Glorot, X., Weston, J., Bengio, Y.: A semantic matching energy function for learning with multi-relational data - application to word-sense disambiguation. Mach. Learn. 94(2), 233–259 (2014)

    Article  MathSciNet  Google Scholar 

  2. Bordes, A., Usunier, N., García-Durán, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: NIPS, pp. 2787–2795 (2013)

    Google Scholar 

  3. Bordes, A., Weston, J., Collobert, R., Bengio, Y.: Learning structured embeddings of knowledge bases. In: AAAI (2011)

    Google Scholar 

  4. Demeester, T., Rocktäschel, T., Riedel, S.: Lifted rule injection for relation embeddings. In: EMNLP, pp. 1389–1399 (2016)

    Google Scholar 

  5. Du, J., Qi, K., Wan, H., Peng, B., Lu, S., Shen, Y.: Enhancing knowledge graph embedding from a logical perspective. In: Wang, Z., Turhan, A.-Y., Wang, K., Zhang, X. (eds.) JIST 2017. LNCS, vol. 10675, pp. 232–247. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70682-5_15

    Chapter  Google Scholar 

  6. Grau, B.C., Horrocks, I., Motik, B., Parsia, B., Patel-Schneider, P.F., Sattler, U.: OWL 2: the next step for OWL. J. Web Semant. 6(4), 309–322 (2008)

    Article  Google Scholar 

  7. Guo, S., Wang, Q., Wang, L., Wang, B., Guo, L.: Jointly embedding knowledge graphs and logical rules. In: EMNLP, pp. 192–202 (2016)

    Google Scholar 

  8. Guo, S., Wang, Q., Wang, L., Wang, B., Guo, L.: Knowledge graph embedding with iterative guidance from soft rules. In: AAAI, pp. 4816–4823 (2018)

    Google Scholar 

  9. He, S., Liu, K., Ji, G., Zhao, J.: Learning to represent knowledge graphs with Gaussian embedding. In: CIKM, pp. 623–632 (2015)

    Google Scholar 

  10. Jenatton, R., Roux, N.L., Bordes, A., Obozinski, G.: A latent factor model for highly multi-relational data. In: NIPS, pp. 3176–3184 (2012)

    Google Scholar 

  11. Ji, G., He, S., Xu, L., Liu, K., Zhao, J.: Knowledge graph embedding via dynamic mapping matrix. In: ACL, pp. 687–696 (2015)

    Google Scholar 

  12. Ji, G., Liu, K., He, S., Zhao, J.: Knowledge graph completion with adaptive sparse transfer matrix. In: AAAI, pp. 985–991 (2016)

    Google Scholar 

  13. Jia, Y., Wang, Y., Lin, H., Jin, X., Cheng, X.: Locally adaptive translation for knowledge graph embedding. In: AAAI, pp. 992–998 (2016)

    Google Scholar 

  14. Lin, Y., Liu, Z., Luan, H., Sun, M., Rao, S., Liu, S.: Modeling relation paths for representation learning of knowledge bases. In: EMNLP, pp. 705–714 (2015)

    Google Scholar 

  15. Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: AAAI, pp. 2181–2187 (2015)

    Google Scholar 

  16. Liu, H., Wu, Y., Yang, Y.: Analogical inference for multi-relational embeddings. In: ICML, pp. 2168–2178 (2017)

    Google Scholar 

  17. Nickel, M., Rosasco, L., Poggio, T.A.: Holographic embeddings of knowledge graphs. In: AAAI, pp. 1955–1961 (2016)

    Google Scholar 

  18. Pérez-Urbina, H., Motik, B., Horrocks, I.: Tractable query answering and rewriting under description logic constraints. J. Appl. Logic 8(2), 186–209 (2010)

    Article  MathSciNet  Google Scholar 

  19. Rocktäschel, T., Singh, S., Riedel, S.: Injecting logical background knowledge into embeddings for relation extraction. In: NAACL, pp. 1119–1129 (2015)

    Google Scholar 

  20. Socher, R., Chen, D., Manning, C.D., Ng, A.Y.: Reasoning with neural tensor networks for knowledge base completion. In: NIPS, pp. 926–934 (2013)

    Google Scholar 

  21. Trouillon, T., Welbl, J., Riedel, S., Gaussier, É., Bouchard, G.: Complex embeddings for simple link prediction. In: ICML, pp. 2071–2080 (2016)

    Google Scholar 

  22. Wang, Q., Mao, Z., Wang, B., Guo, L.: Knowledge graph embedding: a survey of approaches and applications. IEEE Trans. Knowl. Data Eng. 29(12), 2724–2743 (2017)

    Article  Google Scholar 

  23. Wang, Q., Wang, B., Guo, L.: Knowledge base completion using embeddings and rules. In: IJCAI, pp. 1859–1866 (2015)

    Google Scholar 

  24. Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: AAAI, pp. 1112–1119 (2014)

    Google Scholar 

  25. Wei, Z., Zhao, J., Liu, K., Qi, Z., Sun, Z., Tian, G.: Large-scale knowledge base completion: Inferring via grounding network sampling over selected instances. In: CIKM, pp. 1331–1340 (2015)

    Google Scholar 

  26. Xiao, H., Huang, M., Zhu, X.: TransG: a generative model for knowledge graph embedding. In: ACL, pp. 992–998 (2016)

    Google Scholar 

  27. Yang, B., Yih, W., He, X., Gao, J., Deng, L.: Embedding entities and relations for learning and inference in knowledge bases. CoRR abs/1412.6575 (2014). http://arxiv.org/abs/1412.6575

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Acknowledgements

This work was partly supported by National Natural Science Foundation of China (61375056 and 61876204), Science and Technology Program of Guangzhou (201804010496), and Scientific Research Innovation Team in Department of Education of Guangdong Province (2017KCXTD013).

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Authors and Affiliations

  1. Guangdong University of Foreign Studies, Guangzhou, 510006, China

    Jianfeng Du, Kunxun Qi & Yuming Shen

Authors
  1. Jianfeng Du
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  2. Kunxun Qi
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  3. Yuming Shen
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Corresponding author

Correspondence to Jianfeng Du .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Maosong Sun

  2. Harbin Institute of Technology, Harbin, China

    Ting Liu

  3. Beijing University of Posts and Telecommunications, Beijing, China

    Xiaojie Wang

  4. Tsinghua University, Beijing, China

    Zhiyuan Liu

  5. Tsinghua University, Beijing, China

    Yang Liu

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Du, J., Qi, K., Shen, Y. (2018). Knowledge Graph Embedding with Logical Consistency. In: Sun, M., Liu, T., Wang, X., Liu, Z., Liu, Y. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. CCL NLP-NABD 2018 2018. Lecture Notes in Computer Science(), vol 11221. Springer, Cham. https://doi.org/10.1007/978-3-030-01716-3_11

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  • DOI: https://doi.org/10.1007/978-3-030-01716-3_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01715-6

  • Online ISBN: 978-3-030-01716-3

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