Abstract
We propose a staged framework for question answering over a large-scale structured knowledge base. Following existing methods based on semantic parsing, our method relies on various components for solving different sub-tasks of the problem. In the first stage, we directly use the result of entity linking to obtain the topic entity in a question, and simplify the process as a semantic matching problem. We train a neural network to match questions and predicate sequences to get a rough set of candidate answer entities from the knowledge base. Unlike traditional methods, we also consider entity type as a constraint on candidate answers to remove wrong candidates from the rough set in the second stage. By applying a convolutional neural network model to match questions and predicate sequences and a type constraint to filter candidate answers, our method achieves an average F1 measure of 74.8% on the WEBQUESTIONSSP dataset, it is competitive with state-of-the-art semantic parsing approaches.
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Notes
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Available at http://aka.ms/WebQSP.
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References
Bollacker, K., Evans, C., Paritosh, P., et al.: 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, New York (2008)
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
Xiao, Y., Wang, H., Song, Y., Hwang, S., Wang, W.: KBQA: learning question answering over QA corpora and knowledge bases. In: Proceedings of the 42nd International Conference on Very Large Data Bases, New Delhi, India, pp. 565–575. ACM (2016)
Denis, L., Asja, F., et al.: Neural network-based question answering over knowledge graphs on word and character level. In: 26th International World Wide Web Conference, Perth, Australia, pp. 1211–1220 (2017)
Yih, S.W.T., Chang, M.W., et al.: Semantic parsing via staged query graph generation: question answering with knowledge base. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics, Beijing, China, pp. 1321–1331. Association for Computational Linguistics (2015)
Yih, W., Richardson, M., Meek, C., et al.: The value of semantic parse labeling for knowledge base question answering. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Berlin, Germany, pp. 201–206. Association for Computational Linguistics (2016)
Dai, Z., Li, L., Xu, W.: CFO: conditional focused neural question answering with large scale knowledge bases. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Berlin, Germany, pp. 800–810. Association for Computational Linguistics (2016)
Yu, M.: Improved neural relation detection for knowledge base question answering. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Vancouver, BC, Canada, pp. 571–581. Association for Computational Linguistics (2017)
Qu, Y., Liu, J., Kang, L., et al.: Question answering over freebase via attentive RNN with similarity matrix based CNN. arXiv preprint arXiv:1804.03317 (2018)
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, Seattle, Washington, USA, pp. 1533–1544. Association for Computational Linguistics (2013)
Reddy, S., Lapata, M., Steedman, M.: Large-scale semantic parsing without question-answer pairs. Trans. Assoc. Comput. Linguist. 2(2014), 377–394 (2014)
Fader, A., Zettlemoyer, L., Etzioni, O.: Open question answering over curated and extracted knowledge bases. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp. 1156–1165. ACM, New York (2014)
Berant, J., Liang, P.: Semantic parsing via paraphrasing. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, Baltimore, Maryland, pp. 1415–1425. Association for Computational Linguistics (2014)
Yan, X., Mou, L., Li, G., et al.: Classifying relations via long short term memory networks along shortest dependency path. In: The 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal, pp. 1785–1794. Association for Computational Linguistics (2015)
Dong, L., Lapata, M.: Language to logical form with neural attention. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Berlin, Germany, pp. 33–43. Association for Computational Linguistics (2016)
Zeng, D., Liu, K., Lai, S., et al.: Relation classification via convolutional deep neural network. In: The 25th International Conference on Computational Linguistics: Technical Papers, Dublin, Ireland, pp. 2335–2344. ACM (2014)
Shen, Y., He, X., Gao, J., Deng, L., Mesnil, G.: A latent semantic model with convolutional-pooling structure for information retrieval. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, Shanghai, China, pp. 101–110. ACM (2014)
Dong, L., Wei, F., Zhou, M., Xu, K.: Question answering over freebase with multi-column convolutional neural networks. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, Beijing, China, pp 260–269 (2015)
Gao, J., Pantel, P., Gamon, M., He, X., Deng, L., Shen, Y.: Modeling interestingness with deep neural networks. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (2014)
Huang, P., He, X., Gao, J., Deng, L., Acero, A., Heck, L.: Learning deep structured semantic models for web search using clickthrough data. In Proceedings of the 22nd ACM International Conference on Conference on information and knowledge management, San Francisco, CA, USA, pp. 2333–2338. ACM (2013)
Liang, C., Beranty, J., Le, Q., Forbus, K.D., Lao, N.: Neural symbolic machines: learning semantic parsers on freebase with weak supervision. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Vancouver, BC, Canada, pp. 23–33. Association for Computational Linguistics (2017)
Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, Doha, Qatar, pp 1532–1543. Association for Computational Linguistics (2014)
Acknowledgements
The work is supported by the Natural Science Foundation of China under grant No. 61502095, and the Natural Science Foundation of Jiangsu Province under Grant BK20140643.
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Chen, Y., Li, H., Xu, Z. (2019). Convolutional Neural Network-Based Question Answering Over Knowledge Base with Type Constraint. In: Zhao, J., Harmelen, F., Tang, J., Han, X., Wang, Q., Li, X. (eds) Knowledge Graph and Semantic Computing. Knowledge Computing and Language Understanding. CCKS 2018. Communications in Computer and Information Science, vol 957. Springer, Singapore. https://doi.org/10.1007/978-981-13-3146-6_3
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