Abstract
Building system that could answer questions in natural language is one of the most important natural language processing applications. Recently, the raise of large-scale open-domain knowledge base provides a new possible approach. Some existing systems conduct question-answering relaying on hand-craft features and rules, other work try to extract features by popular neural networks. In this paper, we adopt recurrent neural network to understand questions and find out the corresponding answer entities from knowledge bases based on word embedding and knowledge bases embedding. Question-answer pairs are used to train our multi-step system. We evaluate our system on FREEBASE and WEBQUESTIONS. The experimental results show that our system achieves comparable performance compared with baseline method with a more straightforward structure.
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
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). doi:10.1007/978-3-540-76298-0_52
Bao, J., Duan, N., Zhou, M., Zhao, T.: Knowledge-based question answering as machine translation. Cell 2(6) (2014)
Bast, H., Haussmann, E.: More accurate question answering on freebase. In: Proceedings of 24th ACM International on Conference on Information and Knowledge Management, pp. 1431–1440. ACM (2015)
Berant, J., Chou, A., Frostig, R., Liang, P.: Semantic parsing on freebase from question-answer pairs. In: EMNLP, p. 6 (2013)
Berant, J., Liang, P.: Semantic parsing via paraphrasing. In: ACL, no. 1, pp. 1415–1425 (2014)
Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of 2008 ACM SIGMOD International Conference on Management of Data, pp. 1247–1250. ACM (2008)
Bordes, A., Chopra, S., Weston, J.: Question answering with subgraph embeddings (2014). arXiv preprint arXiv:1406.3676
Bordes, A., Usunier, N., Chopra, S., Weston, J.: Large-scale simple question answering with memory networks (2015). arXiv preprint arXiv:1506.02075
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). doi:10.1007/978-3-662-44848-9_11
Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using RNN encoder-decoder for statistical machine translation (2014). arXiv preprint arXiv:1406.1078
Dong, L., Wei, F., Zhou, M., Xu, K.: Question answering over freebase with multi-column convolutional neural networks. In: Proceedings of 53rd Annual Meeting of the Association for Computational Linguistics and 7th International Joint Conference on Natural Language Processing, vol. 1, pp. 260–269 (2015)
Golub, D., He, X.: Character-level question answering with attention (2016). arXiv preprint arXiv:1604.00727
Kingma, D., Ba, J.: Adam: a method for stochastic optimization (2014). arXiv preprint arXiv:1412.6980
Liang, P., Jordan, M.I., Klein, D.: Learning dependency-based compositional semantics. In: Proceedings of 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 590–599. Association for Computational Linguistics (2011)
Ling, X., Singh, S., Weld, D.S.: Design challenges for entity linking. Trans. Assoc. Comput. Linguist. 3, 315–328 (2015)
Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J.R., Bethard, S., McClosky, D.: The Stanford CoreNLP natural language processing toolkit. In: ACL (System Demonstrations), pp. 55–60 (2014)
Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge. In: Proceedings of 16th International Conference on World Wide Web, pp. 697–706. ACM (2007)
Yao, X., Van Durme, B.: Information extraction over structured data: question answering with freebase. In: ACL, no. 1, pp. 956–966. Citeseer (2014)
Yih, W.T., Chang, M.W., He, X., Gao, J.: Semantic parsing via staged query graph generation: question answering with knowledge base. In: Association for Computational Linguistics (ACL) (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Chen, S., Wen, J., Zhang, R. (2016). GRU-RNN Based Question Answering Over Knowledge Base. In: Chen, H., Ji, H., Sun, L., Wang, H., Qian, T., Ruan, T. (eds) Knowledge Graph and Semantic Computing: Semantic, Knowledge, and Linked Big Data. CCKS 2016. Communications in Computer and Information Science, vol 650. Springer, Singapore. https://doi.org/10.1007/978-981-10-3168-7_8
Download citation
DOI: https://doi.org/10.1007/978-981-10-3168-7_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-3167-0
Online ISBN: 978-981-10-3168-7
eBook Packages: Computer ScienceComputer Science (R0)