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Retrieving Relationships from a Knowledge Graph for Question Answering

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Advances in Information Retrieval (ECIR 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11437))

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Abstract

Answering natural language questions posed on a knowledge graph requires traversing an appropriate sequence of relationships starting from the mentioned entities. To answer complex queries, we often need to traverse more than two relationships. Traditional approaches traverse at most two relationships, as well as typically first retrieve candidate sets of relationships using indexing etc., which are then compared via machine-learning. Such approaches rely on the textual labels of the relationships, rather than the structure of the knowledge graph. In this paper, we present a novel approach KG-REP that directly predicts the embeddings of the target relationships against a natural language query, avoiding the candidate retrieval step, using a sequence to sequence neural network. Our model takes into account the knowledge graph structure via novel entity and relationship embeddings. We release a new dataset containing complex queries on a public knowledge graph that typically require traversal of as many as four relationships to answer. We also present a new benchmark result on a public dataset for this problem.

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References

  1. Agarwal, P., Ramanath, M., Shroff, G.: Relationship queries on large graphs. In: Proceedings of International Conference on Management of Data. COMAD (2017)

    Google Scholar 

  2. Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: DBpedia: a nucleus for a web of open data. In: Aberer, K., et al. (eds.) ASWC/ISWC-2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-76298-0_52

    Chapter  Google Scholar 

  3. Belkin, M., Niyogi, P.: Laplacian eigenmaps and spectral techniques for embedding and clustering. In: Advances in Neural Information Processing Systems, pp. 585–591. NIPS (2002)

    Google Scholar 

  4. Berant, J., Chou, A., Frostig, R., Liang, P.: Semantic parsing on freebase from question-answer pairs. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, EMNLP (2013)

    Google Scholar 

  5. Berant, J., Liang, P.: Semantic parsing via paraphrasing. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). vol. 1, pp. 1415–1425 (2014)

    Google Scholar 

  6. Berant, J., Liang, P.: Imitation learning of agenda-based semantic parsers. Trans. Assoc. Comput. Linguist. 3, 545–558 (2015)

    Article  Google Scholar 

  7. 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. SIGMOD. ACM (2008)

    Google Scholar 

  8. Bordes, A., Chopra, S., Weston, J.: Question answering with subgraph embeddings. arXiv preprint arXiv:1406.3676 (2014)

  9. Bordes, A., Usunier, N., Chopra, S., Weston, J.: Large-scale simple question answering with memory networks. arXiv preprint arXiv:1506.02075 (2015)

  10. 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. NIPS (2013)

    Google Scholar 

  11. Bordes, A., Weston, J., Collobert, R., Bengio, Y.: Learning structured embeddings of knowledge bases. In: Conference on artificial intelligence, No. EPFL-CONF-192344 (2011)

    Google Scholar 

  12. Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka Jr., E.R., Mitchell, T.M.: Toward an architecture for never-ending language learning. In: Proceedings of AAAI Conference on Artificial Intelligence. AAAI, vol. 5, p. 3. Atlanta (2010)

    Google Scholar 

  13. Chang, W.t.Y.M.W., Gao, X.H.J.: Semantic parsing via staged query graph generation: question answering with knowledge base. In: Proceedings of the 53rd Annual Meeting on Association for Computational Linguistics. ACL (2015)

    Google Scholar 

  14. Dai, Z., Li, L., Xu, W.: Cfo: conditional focused neural question answering with large-scale knowledge bases (2016)

    Google Scholar 

  15. Do, T.T., Cheung, N.M.: Embedding based on function approximation for large scale image search. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 626–638 (2018)

    Article  Google Scholar 

  16. Dubey, M., Banerjee, D., Chaudhuri, D., Lehmann, J.: EARL: joint entity and relation linking for question answering over knowledge graphs. In: Vrandečić, D., et al. (eds.) ISWC 2018, Part I. LNCS, vol. 11136, pp. 108–126. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00671-6_7

    Chapter  Google Scholar 

  17. Fan, M., Zhou, Q., Chang, E., Zheng, T.F.: Transition-based knowledge graph embedding with relational mapping properties. In: Proceedings of the 28th Pacific Asia Conference on Language, Information and Computing (2014)

    Google Scholar 

  18. Furcy, D., Koenig, S.: Limited discrepancy beam search. In: Proceedings of the International Joint Conferences on Artificial Intelligence, pp. 125–131 (2005)

    Google Scholar 

  19. Golub, D., He, X.: Character-level question answering with attention. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. EMNLP (2016)

    Google Scholar 

  20. Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD, ACM (2016)

    Google Scholar 

  21. Guu, K., Miller, J., Liang, P.: Traversing Knowledge Graphs in Vector Space (2015)

    Google Scholar 

  22. Hatcher, E., Gospodnetic, O., McCandless, M.: Lucene in Action. Manning Publications, Greenwich (2004)

    Google Scholar 

  23. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  24. Jain, S.: Question answering over knowledge base using factual memory networks. In: Proceedings of the NAACL Student Research Workshop (2016)

    Google Scholar 

  25. Ji, G., He, S., Xu, L., Liu, K., Zhao, J.: Knowledge graph embedding via dynamic mapping matrix. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), vol. 1, pp. 687–696 (2015)

    Google Scholar 

  26. Lai, S., Xu, L., Liu, K., Zhao, J.: Recurrent convolutional neural networks for text classification. In: Proceedings of AAAI Conference on Artificial Intelligence. AAAI (2015)

    Google Scholar 

  27. Li, R.H., Qin, L., Xu Yu, J., Mao, R.: Efficient and progressive group steiner tree search. In: International Conference on Database Theory. SIGMOD (2016)

    Google Scholar 

  28. Li, Y., Ping, W.: Cancer metastasis detection with neural conditional random field. In: Medical Imaging with Deep Learning (2018)

    Google Scholar 

  29. Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of AAAI Conference on Artificial Intelligence. AAAI, vol. 15, pp. 2181–2187 (2015)

    Google Scholar 

  30. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems. NIPS (2013)

    Google Scholar 

  31. Nickel, M., Tresp, V., Kriegel, H.P.: A three-way model for collective learning on multi-relational data. ICML 11, 809–816 (2011)

    Google Scholar 

  32. Pennington, J., Socher, R., Manning, C.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. EMNLP (2014)

    Google Scholar 

  33. Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD (2014)

    Google Scholar 

  34. Radhakrishnan, P., Talukdar, P., Varma, V.: Elden: Improved entity linking using densified knowledge graphs. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), vol. 1, pp. 1844–1853 (2018)

    Google Scholar 

  35. Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)

    Article  Google Scholar 

  36. Singh, M.P., et al.: Knadia: enterprise knowledge assisted dialogue systems using deep learning. In: Proceedings of International Conference on Data Engineering. ICDE (2018)

    Google Scholar 

  37. 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. NIPS (2013)

    Google Scholar 

  38. Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge. In: Proceedings of the 16th International Conference on World Wide Web, WWW. pp. 697–706. ACM (2007)

    Google Scholar 

  39. Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems. NIPS (2014)

    Google Scholar 

  40. Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)

    Article  Google Scholar 

  41. Wang, Y., Berant, J., Liang, P.: Building a semantic parser overnight. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), vol. 1, pp. 1332–1342 (2015)

    Google Scholar 

  42. Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: Proceedings of AAAI Conference on Artificial Intelligence. AAAI, vol. 14, pp. 1112–1119 (2014)

    Google Scholar 

  43. Wang, Z., Li, J.Z.: Text-enhanced representation learning for knowledge graph. In: Proceedings of the International Joint Conferences on Artificial Intelligence, pp. 1293–1299. IJCAI (2016)

    Google Scholar 

  44. Wang, Z., Chen, T., Ren, J.S.J., Yu, W., Cheng, H., Lin, L.: Deep reasoning with knowledge graph for social relationship understanding. In: Proceedings of the International Joint Conferences on Artificial Intelligence. IJCAI (2018)

    Google Scholar 

  45. Xiao, H., Huang, M., Zhu, X.: Transg: A generative model for knowledge graph embedding. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL, vol. 1, pp. 2316–2325 (2016)

    Google Scholar 

  46. Xie, R., Liu, Z., Jia, J., Luan, H., Sun, M.: Representation learning of knowledge graphs with entity descriptions. In: Proceedings of AAAI Conference on Artificial Intelligence, pp. 2659–2665. AAAI (2016)

    Google Scholar 

  47. Yahya, M., Berberich, K., Elbassuoni, S., Ramanath, M., Tresp, V., Weikum, G.: Natural language questions for the web of data. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. EMNLP-CoNLL, pp. 379–390 (2012)

    Google Scholar 

  48. Yang, Y., Chang, M.W.: S-mart: novel tree-based structured learning algorithms applied to tweet entity linking. In: Proceedings of the 53rd Annual Meeting on Association for Computational Linguistics. ACL (2015)

    Google Scholar 

  49. Yih, W.t., Richardson, M., Meek, C., Chang, M.W., Suh, J.: The value of semantic parse labeling for knowledge base question answering. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), vol. 2, pp. 201–206 (2016)

    Google Scholar 

  50. Yin, W., Yu, M., Xiang, B., Zhou, B., Schütze, H.: Simple question answering by attentive convolutional neural network. In: Proceedings of the 26th International Conference on Computational Linguistics. COLING (2016)

    Google Scholar 

  51. Yu, M., Yin, W., Hasan, K.S., Santos, C.d., Xiang, B., Zhou, B.: Improved neural relation detection for knowledge base question answering. In: Proceedings of the 55th Annual Meeting on Association for Computational Linguistics. ACL (2017)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Indian Institute of Technology, Hauz Khas, New Delhi, India

    Puneet Agarwal & Maya Ramanath

  2. TCS Research, New Delhi, India

    Puneet Agarwal & Gautam Shroff

Authors
  1. Puneet Agarwal
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  2. Maya Ramanath
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  3. Gautam Shroff
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Corresponding author

Correspondence to Puneet Agarwal .

Editor information

Editors and Affiliations

  1. University of Strathclyde, Glasgow, UK

    Leif Azzopardi

  2. Bauhaus Universität Weimar, Weimar, Germany

    Benno Stein

  3. Universität Duisburg-Essen, Duisburg, Germany

    Norbert Fuhr

  4. GESIS - Leibniz Institute for the Social Sciences, Cologne, Germany

    Philipp Mayr

  5. Delft University of Technology, Delft, The Netherlands

    Claudia Hauff

  6. University of Twente, Enschede, The Netherlands

    Djoerd Hiemstra

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Agarwal, P., Ramanath, M., Shroff, G. (2019). Retrieving Relationships from a Knowledge Graph for Question Answering. In: Azzopardi, L., Stein, B., Fuhr, N., Mayr, P., Hauff, C., Hiemstra, D. (eds) Advances in Information Retrieval. ECIR 2019. Lecture Notes in Computer Science(), vol 11437. Springer, Cham. https://doi.org/10.1007/978-3-030-15712-8_3

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  • DOI: https://doi.org/10.1007/978-3-030-15712-8_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-15711-1

  • Online ISBN: 978-3-030-15712-8

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