Abstract
Question answering over knowledge graph has attracted increasing attention. Though the previous algorithms have achieved competitive performance, they fail to solve problems like humans resulting in the bottleneck of reasoning. However, it is difficult for machines to simulate the question answering process of humans. In order to address this challenge, we propose a novel Cognitive Knowledge Graph Reasoning (CKGR) model based on the cognitive architecture for complex question answering. The CKGR processes information hierarchically with a three-level framework. To fully analyze the question, the first level is proposed to transform the question into features according to different aspects. Then, the relative knowledge graph (KG) regions are activated to simulate the human unconscious thinking process by a memory mapping module. Finally, the CKGR goes deeper to infer the correct answer over KG considering the both semantic and logical parsing of the questions. The CKGR successfully narrows the gap between humans and machines. Extensive experiments on three real-world datasets demonstrate that the proposed method achieves better performance compared with the state-of-the-art methods and provides the reasoning score to find the reasonable path for the answer.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Yani, M., Krisnadhi, A.A.: Challenges, techniques, and trends of simple knowledge graph question answering: a survey. Information 12, 271 (2021)
Wang, Z., Ng, P., Nallapati, R., Xiang, B.: Retrieval, re-ranking and multi-task learning for knowledge-base question answering. In: EACL, pp. 347–357 (2021)
Bhutani, N., Zheng, X., Qian, K., Li, Y., Jagadish, H.: Answering complex questions by combining information from curated and extracted knowledge bases. In: Proceedings of the First Workshop on Natural Language Interfaces, pp. 1–10 (2020)
Chen, Y., Li, H., Hua, Y., Qi, G.: Formal query building with query structure prediction for complex question answering over knowledge base. In: IJCAI, pp. 3751–3758 (2020)
Miller, A., Fisch, A., Dodge, J., Karimi, A.-H., Bordes, A., Weston, J.: Key-value memory networks for directly reading documents arXiv preprint arXiv:1606.03126 (2016)
Zhang, Y., Dai, H., Kozareva, Z., Smola, A.J., Song, L.: Variational reasoning for question answering with knowledge graph. In: AAAI (2018)
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)
Shettleworth, S.J.: Cognition, Evolution, and Behavior. Oxford university press, Oxford (2009)
Wang, Y., Patel, S., Patel, D., Wang, Y.: A layered reference model of the brain. In: The Second IEEE International Conference on Cognitive Informatics, pp. 7–17 (2003)
Wang, Y., Ruhe, G.: The cognitive process of decision making. Int. J. Cogn. Inf. Nat. Intell. (IJCINI) 1, 73–85 (2007)
Ritter, F.E., Tehranchi, F., Oury, J.D.: Act-r: a cognitive architecture for modeling cognition. Wiley Interdisc. Rev.: Cognitive Sci. 10, e1488 (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. arXiv preprint arXiv:1809.00782Â (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)
Sun, H., Bedrax-Weiss, T., Cohen, W.W.: Pullnet: open domain question answering with iterative retrieval on knowledge bases and text arXiv preprint arXiv:1904.09537Â (2019)
Anderson, J.R., Matessa, M., Lebiere, C.: Act-r: a theory of higher level cognition and its relation to visual attention. Hum.-Comput. Interact. 12, 439–462 (1997)
Laird, J.E., Newell, A., Rosenbloom, P.S.: Soar: an architecture for general intelligence. Artif. Intell. 33, 1–64 (1987)
Zellers, R., Bisk, Y., Farhadi, A., Choi, Y.: From recognition to cognition: visual commonsense reasoning. In: CVPR, pp. 6720–6731 (2019)
Honnibal, M., Montani, I.: Spacy 2: natural language understanding with bloom embeddings, convolutional neural networks and incremental parsing. To appear 7, 411–420 (2017)
Liu, Y., et al.: A robustly optimized bert pretraining approach arXiv preprint arXiv:1907.11692Â (2019)
Yih, S.W.-T., Chang, M.-W., He, X., Gao, J.: Semantic parsing via staged query graph generation: Question answering with knowledge base (2015)
Talmor, A., Berant, J.: The web as a knowledge-base for answering complex questions arXiv preprint arXiv:1803.06643Â (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zhao, H., Fu, Y., Jiang, W., Pu, S., Cai, X. (2022). Simulate Human Thinking: Cognitive Knowledge Graph Reasoning for Complex Question Answering. In: Gama, J., Li, T., Yu, Y., Chen, E., Zheng, Y., Teng, F. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2022. Lecture Notes in Computer Science(), vol 13280. Springer, Cham. https://doi.org/10.1007/978-3-031-05933-9_41
Download citation
DOI: https://doi.org/10.1007/978-3-031-05933-9_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-05932-2
Online ISBN: 978-3-031-05933-9
eBook Packages: Computer ScienceComputer Science (R0)