Abstract
This paper focuses on the problem of question answering over knowledge graph (KG-QA). With the increasing availability of different knowledge graphs in a variety of domains, KG-QA becomes a prevalent information interaction approach. Current KG-QA methods usually resort to semantic parsing, retrieval or neural matching based models. However, current methods generally ignore the rich domain context, i.e., category and surrounding descriptions within the knowledge graphs. Experiments shows that they can not well tackle the complex questions and information needs.
In this work, we propose a new KG-QA approach, leveraging the domain context. The new method designs a neural cross-attention QA framework. We incorporate the new approach with question and answer domain contexts. Specifically, for questions, we enrich them with users’ access log, and for the answers, we equip them with meta-paths within the target knowledge graph. Experimental study on real datasets verifies its improvement. The new approach is especially beneficial for domain knowledge graphs.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
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., Chopra, S., Weston, J.: Question answering with subgraph embeddings. In: Proceedings of EMNLP, pp. 615–620 (2014)
Bordes, A., Usunier, N., Chopra, S., Weston, J.: Large-scale simple question answering with memory networks. arXiv preprint arXiv:1506.02075 (2015)
Bordes, A., Usunier, N., GarcĂa-Durán, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Proceedings of NIPS, pp. 2787–2795 (2013)
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)
Dong, L., Wei, F., Zhou, M., Xu, K.: Question answering over freebase with multi-column convolutional neural networks. In: Proceedings of ACL, pp. 260–269 (2015)
Dong, Y., Chawla, N.V., Swami, A.: Metapath2vec: scalable representation learning for heterogeneous networks. In: Proceedings of KDD, pp. 135–144 (2017)
Fader, A., Zettlemoyer, L., Etzioni, O.: Open question answering over curated and extracted knowledge bases. In: Proceedings of KDD, pp. 1156–1165 (2014)
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)
Hermann, K.M., et al.: Teaching machines to read and comprehend. In: Proceedings of NIPS, pp. 1693–1701 (2015)
Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014)
Kwiatkowski, T., Zettlemoyer, L.S., Goldwater, S., Steedman, M.: Inducing probabilistic CCG grammars from logical form with higher-order unification. In: Proceedings of EMNLP, pp. 1223–1233 (2010)
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)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: ICLR (2013)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Proceedings of NIPS, pp. 3111–3119 (2013)
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., He, X., Meek, C.: Semantic parsing for single-relation question answering. In: Proceedings of ACL, pp. 643–648 (2014)
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)
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)
Acknowledgement
This work is supported by NSFC 61502169, U1509219 and SHEITC.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Tong, P., Yao, J., He, L., Xu, L. (2019). Leveraging Domain Context for Question Answering over Knowledge Graph. In: Shao, J., Yiu, M., Toyoda, M., Zhang, D., Wang, W., Cui, B. (eds) Web and Big Data. APWeb-WAIM 2019. Lecture Notes in Computer Science(), vol 11641. Springer, Cham. https://doi.org/10.1007/978-3-030-26072-9_27
Download citation
DOI: https://doi.org/10.1007/978-3-030-26072-9_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-26071-2
Online ISBN: 978-3-030-26072-9
eBook Packages: Computer ScienceComputer Science (R0)