Abstract
Question Answering over Knowledge Graph (KG-QA) becomes a convenient way to interact with the prevailing information. The user’s information needs, i.e., input questions become more complex. We find that the comparison, relation, and opinion questions are witnessed a significant growth, especially in some domains. However, most of the current KG-QA methods cannot appropriately handle the inherent complex relation and coverage characteristics within the questions.
In this work, we propose to utilize the relation information with the questions and knowledge graph in a mutual way, improving the final question answering performance. Wse design local and global attention models for relation detection. We combine the features for relation detection in an attention matching model. Experiments on our new dataset and common dataset reveal its advantages both in accuracy and efficiency.
J. Yao—This work was supported by National Key R&D Program of China (No. 2017YFC0803700), NSFC grant (61972151, 61972155).
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References
Berant, J., Chou, A., Frostig, R., Liang, P.: Semantic parsing on freebase from question-answer pairs. In: Proceedings of EMNLP, pp. 1533–1544 (2013)
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)
Bordes, A., Weston, J., Usunier, N.: Open question answering with weakly supervised embedding models. In: Calders, T., Esposito, F., Hüllermeier, E., Meo, R. (eds.) ECML PKDD 2014. LNCS (LNAI), vol. 8724, pp. 165–180. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44848-9_11
Cai, Q., Yates, A.: Large-scale semantic parsing via schema matching and lexicon extension. In: Proceedings of ACL, pp. 423–433 (2013)
Fan, Z., Wei, Z., Li, P., Lan, Y., Huang, X.: A question type driven framework to diversify visual question generation. In: Proceedings of IJCAI, pp. 4048–4054 (2018)
Hao, Y., et al.: An end-to-end model for question answering over knowledge base with cross-attention combining global knowledge. In: Proceedings of ACL, pp. 221–231 (2017)
He, X., Qian, W., Fu, C., Zhu, Y., Cai, D.: Translating embeddings for knowledge graph completion with relation attention mechanism. In: Proceedings of IJCAI, pp. 4286–4292 (2018)
Li, H., Min, M.R., Ge, Y., Kadav, A.: A context-aware attention network for interactive question answering. In: Proceedings of KDD, pp. 927–935 (2017)
Liang, P., Jordan, M.I., Klein, D.: Learning dependency-based compositional semantics. Comput. Linguist. 39(2), 389–446 (2013)
Miyanishi, T., Hirayama, J.i., Kanemura, A., Kawanabe, M.: Answering mixed type questions about daily living episodes. In: Proceedings of IJCAI, pp. 4265–4271 (2018)
Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Proceedings of EMNLP, pp. 1532–1543 (2014)
Saha, A., Pahuja, V., Khapra, M.M., Sankaranarayanan, K., Chandar, S.: Complex sequential question answering: towards learning to converse over linked question answer pairs with a knowledge graph. In: Proceedings of AAAI, pp. 705–713 (2018)
Tai, K.S., Socher, R., Manning, C.D.: Improved semantic representations from tree-structured long short-term memory networks. In: Proceedings of ACL, pp. 1556–1566 (2015)
Yang, M., Duan, N., Zhou, M., Rim, H.: Joint relational embeddings for knowledge-based question answering. In: Proceedings of EMNLP, pp. 645–650 (2014)
Yao, X., Durme, B.V.: Information extraction over structured data: question answering with freebase. In: Proceedings of ACL, pp. 956–966 (2014)
Yih, W., Chang, M., He, X., Gao, J.: Semantic parsing via staged query graph generation: question answering with knowledge base. In: Proceedings of ACL, pp. 1321–1331 (2015)
Yin, J., Zhao, W.X., Li, X.M.: Type-aware question answering over knowledge base with attention-based tree-structured neural networks. J. Comput. Sci. Technol. 32(4), 805–813 (2017)
Yin, W., Yu, M., Xiang, B., Zhou, B., Schütze, H.: Simple question answering by attentive convolutional neural network. In: Proceedings of COLING, pp. 1746–1756 (2016)
Yu, M., Yin, W., Hasan, K.S., dos Santos, C.N., Xiang, B., Zhou, B.: Improved neural relation detection for knowledge base question answering. In: Proceedings of ACL, pp. 571–581 (2017)
Zhang, Y., Dai, H., Kozareva, Z., Smola, A., Song, L.: Variational reasoning for question answering with knowledge graph. In: Proceedings of AAAI, pp. 6069–6076 (2018)
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Zhang, Q., Tong, P., Yao, J., Wang, X. (2020). Mutual Relation Detection for Complex Question Answering over Knowledge Graph. In: Nah, Y., Cui, B., Lee, SW., Yu, J.X., Moon, YS., Whang, S.E. (eds) Database Systems for Advanced Applications. DASFAA 2020. Lecture Notes in Computer Science(), vol 12113. Springer, Cham. https://doi.org/10.1007/978-3-030-59416-9_38
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