Abstract
The topic of knowledge-based question answering (\(\mathsf {KBQA}\)) has attracted wide attention for a long period. A series of techniques have been developed, especially for simple questions. To answer complex questions, most existing approaches apply a semantic parsing-based strategy that parses a question into a query graph for result identification. However, due to poor quality, query graphs often lead to incorrect answers. To tackle the issue, we propose a comprehensive approach for query graph generation, based on two novel models. One leverages attention mechanism with richer information from knowledge base, for core path generation and the other one employs a memory-based network for constraints selection. The experimental results show that our approach outperforms existing methods on typical benchmark datasets.
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Notes
- 1.
\(\langle \)tv.tv_character.appeared_in_tv_program,tv.regular_tv_appearance.actor\(\rangle \).
- 2.
\(\langle \)fictional_universe.fictional_character.character_created_by\(\rangle \).
References
Abujabal, A., Saha Roy, R., Yahya, M., Weikum, G.: Never-ending learning for open-domain question answering over knowledge bases. In: Proceedings of the 2018 World Wide Web Conference, pp. 1053–1062 (2018)
Abujabal, A., Yahya, M., Riedewald, M., Weikum, G.: Automated template generation for question answering over knowledge graphs. In: Proceedings of the 26th International Conference on World Wide Web, pp. 1191–1200 (2017)
Bao, J., Duan, N., Yan, Z., Zhou, M., Zhao, T.: Constraint-based question answering with knowledge graph. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pp. 2503–2514 (2016)
Berant, J., Liang, P.: Semantic parsing via paraphrasing. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 1415–1425 (2014)
Berant, J., Liang, P.: Imitation learning of agenda-based semantic parsers. Trans. Assoc. Comput. Linguistics 3, 545–558 (2015)
Bollacker, K.D., Evans, C., Paritosh, P.K., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the International Conference on Management of Data, pp. 1247–1250 (2008)
Bordes, A., Chopra, S., Weston, J.: Question answering with subgraph embeddings. In: 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, pp. 615–620 (2014)
Chen, Y., Li, H., Hua, Y., Qi, G.: Formal query building with query structure prediction for complex question answering over knowledge base. In: International Joint Conference on Artificial Intelligence (IJCAI) (2020)
Chen, Y., Wu, L., Zaki, M.J.: Bidirectional attentive memory networks for question answering over knowledge bases. In: Proceedings of NAACL-HLT, pp. 2913–2923 (2019)
Ding, J., Hu, W., Xu, Q., Qu, Y.: Leveraging frequent query substructures to generate formal queries for complex question answering. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing and the International Joint Conference on Natural Language Processing, pp. 2614–2622 (2019)
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, pp. 260–269 (2015)
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 (2014)
Han, J., Cheng, B., Wang, X.: Open domain question answering based on text enhanced knowledge graph with hyperedge infusion. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1475–1481 (2020)
Hao, Y., Zhang, Y., Liu, K., He, S., Liu, Z., Wu, H., Zhao, J.: An end-to-end model for question answering over knowledge base with cross-attention combining global knowledge. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pp. 221–231 (2017)
Hu, S., Zou, L., Zhang, X.: A state-transition framework to answer complex questions over knowledge base. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 2098–2108 (2018)
Jain, S.: Question answering over knowledge base using factual memory networks. In: Proceedings of the NAACL Student Research Workshop, pp. 109–115 (2016)
Luo, K., Lin, F., Luo, X., Zhu, K.: Knowledge base question answering via encoding of complex query graphs. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 2185–2194 (2018)
Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge. In: Proceedings of the International Conference on WWW, pp. 697–706 (2007)
Sukhbaatar, S., Weston, J., Fergus, R., et al.: End-to-end memory networks. Advances in neural information processing systems 28 (2015)
Sun, H., Bedrax-Weiss, T., Cohen, W.: Pullnet: open domain question answering with iterative retrieval on knowledge bases and text. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing and the International Joint Conference on Natural Language Processing, pp. 2380–2390 (2019)
Sun, Y., Zhang, L., Cheng, G., Qu, Y.: Sparqa: skeleton-based semantic parsing for complex questions over knowledge bases. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 8952–8959 (2020)
Xu, K., Lai, Y., Feng, Y., Wang, Z.: Enhancing key-value memory neural networks for knowledge based question answering. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 2937–2947 (2019)
Xu, K., Reddy, S., Feng, Y., Huang, S., Zhao, D.: Question answering on freebase via relation extraction and textual evidence. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 2326–2336 (2016)
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 of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, pp. 504–513 (2015)
Yao, X.: Lean question answering over freebase from scratch. In: Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations, pp. 66–70 (2015)
Yao, X., Van Durme, B.: Information extraction over structured data: question answering with freebase. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 956–966 (2014)
Yih, S.W.t., Chang, M.W., He, X., Gao, J.: Semantic parsing via staged query graph generation: question answering with knowledge base (2015)
Yu, M., Yin, W., Hasan, K.S., dos Santos, C., Xiang, B., Zhou, B.: Improved neural relation detection for knowledge base question answering. In: Proceedings of the ACL, pp. 571–581 (2017)
Zhou, M., Huang, M., Zhu, X.: An interpretable reasoning network for multi-relation question answering. In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 2010–2022 (2018)
Zhu, S., Cheng, X., Su, S.: Knowledge-based question answering by tree-to-sequence learning. Neurocomputing 372, 64–72 (2020)
Acknowledgement
This work is supported by Sichuan Scientific Innovation Fund (No. 2022JDRC0009) and the National Key Research and Development Program of China (No. 2017YFA0700800).
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Wang, X., Luo, M., Si, C., Zhan, H. (2022). Answering Complex Questions on Knowledge Graphs. In: Memmi, G., Yang, B., Kong, L., Zhang, T., Qiu, M. (eds) Knowledge Science, Engineering and Management. KSEM 2022. Lecture Notes in Computer Science(), vol 13368. Springer, Cham. https://doi.org/10.1007/978-3-031-10983-6_15
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