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Knowledge Base Completion via Rule-Enhanced Relational Learning

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Knowledge Graph and Semantic Computing: Semantic, Knowledge, and Linked Big Data (CCKS 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 650))

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Abstract

Traditional relational learning techniques perform the knowledge base (KB) completion task based solely on observed facts, ignoring rich domain knowledge that could be extremely useful for inference. In this paper, we encode domain knowledge as simple rules, and propose rule-enhanced relational learning for KB completion. The key idea is to use rules to further refine the inference results given by traditional relational learning techniques, and hence improve the inference accuracy of them. Facts inferred in this way will be the most preferred by relational learning, and at the same time comply with all the rules. Experimental results show that by incorporating the domain knowledge, our approach achieve the best overall performance in the CCKS 2016 competition.

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Notes

  1. 1.

    If the correct answer is not included in the 200 candidates, we give it a rank of 201.

  2. 2.

    http://scikit-learn.org/stable/.

References

  1. Wasserman-Pritsker, E., Cohen, W.W., Minkov, E.: Learning to identify the best contexts for knowledge-based WSD. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1662–1667 (2015)

    Google Scholar 

  2. Hoffmann, R., Zhang, C., Ling, X., Zettlemoyer, L., Weld, D.S.: Knowledgebased weak supervision for information extraction of overlapping relations. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 541–550 (2011)

    Google Scholar 

  3. Nickel, M., Nickel, 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, pp. 809–816 (2011)

    Google Scholar 

  4. Bordes, A., Usunier, N., GarciaDurán, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multirelational data. In: Proceedings of the 27th Annual Conference on Neural Information Processing Systems, pp. 2787–2795 (2013)

    Google Scholar 

  5. Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence, pp. 1112–1119 (2014)

    Google Scholar 

  6. Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence, pp. 2181–2187 (2015)

    Google Scholar 

  7. Guo, S., Wang, Q., Wang, B., Wang, L., Guo, L.: Semantically smooth knowledge graph embedding. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, pp. 84–94 (2015)

    Google Scholar 

  8. Nickel, M., Rosasco, L., Poggio, T.: Holographic embeddings of knowledge graphs. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence, pp. 1955–1961 (2016)

    Google Scholar 

  9. Lao, N., Cohen, W.W.: Relational retrieval using a combination of path-constrained random walks. Mach. Learn. 81(1), 53–67 (2010)

    Article  MathSciNet  Google Scholar 

  10. Lao, N., Mitchell, T., Cohen, W.W.: Random walk inference and learning in a large scale knowledge base. In: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pp. 529–539 (2011)

    Google Scholar 

  11. Wang, Q., Liu, J., Luo, Y., Wang, B., Lin, C.: Knowledge base completion via coupled path ranking. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1308–1318 (2016)

    Google Scholar 

  12. Richardson, M., Domingos, P.: Markov logic networks. Mach. Learn. 62(1–2), 107–136 (2006)

    Article  Google Scholar 

  13. Rocktäschel, T., Singh, S., Riedel, S.: Injecting logical background knowledge into embeddings for relation extraction. In: Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1119–1129 (2015)

    Google Scholar 

  14. Wang, Q., Wang, B., Guo, L.: Knowledge base completion using embeddings and rules. In: Proceedings of the 24th International Joint Conference on Artificial Intelligence, pp. 1859–1865 (2015)

    Google Scholar 

  15. 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: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 1331–1340 (2015)

    Google Scholar 

  16. Jiang, S., Lowd, D., Dou, D.: Learning to refine an automatically extracted knowledge base using markov logic. In: Proceedings of the 2012 IEEE International Conference on Data Mining, pp. 912–917 (2012)

    Google Scholar 

  17. Pujara, J., Miao, H., Getoor, L., Cohen, W.: Knowledge graph identification. In: Alani, H., Kagal, L., Fokoue, A., Groth, P., Biemann, C., Parreira, J.X., Aroyo, L., Noy, N., Welty, C., Janowicz, K. (eds.) ISWC 2013. LNCS, vol. 8218, pp. 542–557. Springer, Heidelberg (2013). doi:10.1007/978-3-642-41335-3_34

    Chapter  Google Scholar 

  18. Guo, S., Wang, Q., Wang, L., Wang, B., Guo, L.: Jointly embedding knowledge graphs and logical rules. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (2016)

    Google Scholar 

  19. Shi, B., Weninger, T.: Fact checking in large knowledge graphs: A discriminative predict path mining approach (2015). arXiv:1510.05911

  20. Gardner, M., Mitchell, T.: Efficient and expressive knowledge base completion using subgraph feature extraction. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1488–1498 (2015)

    Google Scholar 

  21. Wolpert, D.H.: Stacked generalization. Neural Netw. 5, 241–259 (1992)

    Article  Google Scholar 

  22. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  23. Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Mach. Learn. 63(1), 3–42 (2006)

    Article  MATH  Google Scholar 

  24. Chen, T., He, T.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd SIGKDD Conference on Knowledge Discovery and Data Mining (2016)

    Google Scholar 

  25. Niu, X., Sun, X., Wang, H., Rong, S., Qi, G., Yu, Y.: Zhishi.Me: Weaving chinese linking open data. In: Proceedings of the 10th International Conference on the Semantic Web, pp. 205–220 (2011)

    Google Scholar 

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Acknowledgements

We are grateful to the many people who made their code available on-line. We also thank the CCKS 2016 organizers for a fun and exciting competition. This research is supported by the National Natural Science Foundation of China (grant No. 61402465) and the Strategic Priority Research Program of the Chinese Academy of Sciences (grant No. XDA06030200).

Author information

Authors and Affiliations

  1. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China

    Shu Guo, Boyang Ding, Quan Wang & Bin Wang

  2. University of Chinese Academy of Sciences, Beijing, China

    Shu Guo, Boyang Ding, Quan Wang & Bin Wang

  3. National Computer Network Emergency Response Technical Team Coordination Center of China, Beijing, China

    Lihong Wang

Authors
  1. Shu Guo
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  2. Boyang Ding
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  3. Quan Wang
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  4. Lihong Wang
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  5. Bin Wang
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Corresponding author

Correspondence to Quan Wang .

Editor information

Editors and Affiliations

  1. Zhejiang University, Zhejiang, China

    Huajun Chen

  2. Rensselaer Polytechnic Institute, Troy, New York, USA

    Heng Ji

  3. Chinese Academy of Sciences, Beijing, China

    Le Sun

  4. Google Research, Mountain View, California, USA

    Haixun Wang

  5. Wuhan University, Wuhan, Hubei, China

    Tieyun Qian

  6. East China University of Science and Technology, Shanghai, China

    Tong Ruan

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© 2016 Springer Nature Singapore Pte Ltd.

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Guo, S., Ding, B., Wang, Q., Wang, L., Wang, B. (2016). Knowledge Base Completion via Rule-Enhanced Relational Learning. In: Chen, H., Ji, H., Sun, L., Wang, H., Qian, T., Ruan, T. (eds) Knowledge Graph and Semantic Computing: Semantic, Knowledge, and Linked Big Data. CCKS 2016. Communications in Computer and Information Science, vol 650. Springer, Singapore. https://doi.org/10.1007/978-981-10-3168-7_22

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  • DOI: https://doi.org/10.1007/978-981-10-3168-7_22

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3167-0

  • Online ISBN: 978-981-10-3168-7

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