Abstract
Multi-hop Question Answering over Knowledge Graph (multi-hop KGQA) is a challenging task since it requires reasoning with multiple triplets over knowledge graph to find the correct answer entities. Benefiting from expeditious development of attention mechanism and graph neural network, recent works in multi-hop KGQA based on information retrieval have made great progress. However, most existing works focus on encoding questions and knowledge graph in isolation so that the reasoning module lacks interaction of encoding information. Additionally, they only consider matching relation embeddings with the encoded queries at each hop, hence the complex questions, such as those containing one-to-many relation, are hard to answer. In this paper, we propose a query and neighbor-aware reasoning based multi-hop KGQA model to solve the above problems by introducing the CoAttention module and the neighbor-aware reasoning module. Experiments show that our model can not only keep competitive performance on MetaQA datasets but also improve performance than the state-of-the-art baselines on the wide-used benchmarks WebQSP and CWQ.
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References
Berant, J., Chou, A., Frostig, R., Liang, P.: Semantic parsing on freebase from question-answer pairs. In: EMNLP, pp. 1533–1544, October 2013
Bast, H., Haussmann, E. More accurate question answering on freebase. In: ICKM, pp. 1431–1440 (2015)
Abujabal, A., Yahya, M., Riedewald, M., Weikum, G.: Automated template generation for question answering over knowledge graphs. In: WWW, pp. 1191–1200 (2017)
Zhang, Y., Dai, H., Kozareva, Z., Smola, A. J., Song, L.: Variational reasoning for question answering with knowledge graph. In: AAAI, pp. 6069–6076 (2018)
Zhou, M., Huang, M., Zhu, X.: An interpretable reasoning network for multi-relation question answering. In: COLING, pp. 2010–2022 (2018)
Lan, Y., Wang, S., Jiang, J.: Multi-hop knowledge base question answering with an iterative sequence matching model. In: ICDM, pp. 359–368 (2019)
Sun, H., Dhingra, B., Zaheer, M., Mazaitis, K., Salakhutdinov, R., Cohen, W.W.: Open domain question answering using early fusion of knowledge bases and text. In: EMNLP, pp. 4231–4242 (2018)
Sun, H., Bedrax-Weiss, T., Cohen, W.W.: Pullnet: pen domain question answering with iterative retrieval on knowledge bases and text. In: EMNLP/IJCNLP, pp. 2380–2390 (2019)
Qiu, Y., Wang, Y., Jin, X., Zhang, K.: Stepwise reasoning for multi-relation question answering over knowledge graph with weak supervision. In: WSDM, pp. 474–482 (2020)
He, G., Lan, Y., Jiang, J., Zhao, W.X., Wen, J.R:. Improving multi-hop knowledge base question answering by learning intermediate supervision signals. In: WSDM, pp. 553–561 (2021)
Zhong, V., Xiong, C., Keskar, N.S., Socher, R.: Coarse-grain fine-grain coattention network for multi-evidence question answering. In: ICLR (2019)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: ICLR (2017)
Nathani, D., Chauhan, J., Sharma, C., Kaul, M.: Learning attention-based embeddings for relation prediction in knowledge graphs. In: ACL, pp. 4710–4723 (2019)
Luo, K., Lin, F., Luo, X., Zhu, K.: Knowledge base question answering via encoding of complex query graphs. In: EMNLP, pp. 2185–2194 (2018)
Bhutani, N., Zheng, X., Jagadish, H.V.: Learning to answer complex questions over knowledge bases with query composition. In: CIKM, pp. 739–748 (2019)
Lan, Y., Jiang, J.: Query graph generation for answering multi-hop complex questions from knowledge bases. In: ACL, pp. 969–974 (2020)
Schlichtkrull, M., Kipf, T.N., Bloem, P., Van Den Berg, R., Titov, I., Welling, M.: Modeling relational data with graph convolutional networks. In: ESWC, pp. 593–607 (2018)
Saxena, A., Tripathi, A., Talukdar, P.: Improving multi-hop question answering over knowledge graphs using knowledge base embeddings. In: ACL, pp. 4498–4507 (2020)
Miller, A., Fisch, A., Dodge, J., Karimi, A.H., Bordes, A., Weston, J.: Key-value memory networks for directly reading documents. In: EMNLP, pp. 1400–1409 (2016)
Chen, Y., Wu, L., Zaki, M.J.: Bidirectional attentive memory networks for question answering over knowledge bases. In: NAACL, pp. 2913–2923 (2019)
Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: EMNLP, pp. 1532–1543 (2014)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Hudson, D.A., Manning, C.D.: Learning by abstraction: the neural state machine. In: NeurIPS, pp. 5901–5914 (2019)
Zhang, Y., Dai, H., Kozareva, Z., Smola, A.J., Song, L.: Variational reasoning for question answering with knowledge graph. In: AAAI, pp. 6069–6076 (2018)
Deng, Y., Xie, Y., Li, Y., Yang, M., Du, N., Fan, W., Shen, Y.: Multi-task learning with multi-view attention for answer selection and knowledge base question answering. In: AAAI, pp. 6318–6325 (2019)
Yih, S.W.T., Chang, M.W., He, X., Gao, J.: Semantic parsing via staged query graph generation: Question answering with knowledge base. In: ACL, pp. 1321–1331 (2015)
Talmor, A., Berant, J.: The web as a knowledge-base for answering complex questions. In: NAACL, pp. 641–651 (2018)
Kingma, D. P., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2015)
Acknowledgements
This work was in part supported by NSFC (Grant No. 62176194, Grant No.62101 393), the Major project of IoV (Grant No. 2020AAA001), Sanya Science and Education Innovation Park of Wuhan University of Technology (Grant No. 2021KF0 031), CSTC (Grant No. cstc2021jcyj-msxmX1148) and the Open Project of Wuhan University of Technology Chongqing Research Institute (ZL2021-6). We thank MindSpore for the partial support of this work, which is a new deep learning computing framework (https://www.mindspore.cn/).
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Ma, B., Chen, X., Xiong, S. (2022). Query and Neighbor-Aware Reasoning Based Multi-hop Question Answering over Knowledge Graph. 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_11
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