Abstract
Multi-hop reasoning has been widely studied for its important application values in the domain of intelligent search and question answering. Real-world applications are often dominated by natural language input, and it is difficult to directly model the relation semantics in natural language to fit the multi-hop reasoning model. In addition, the extremely large scale and complex structure of knowledge graphs increase the challenge. We propose a natural language and knowledge graph fusion model (NLKGF) for multi-hop reasoning. NLKGF embeds knowledge graph by fusing natural language semantics during the graph propagation process and adds a relation attention mechanism to enable entity representations to perceive the contribution of different relations. A relation path encoder is designed to encode the relation path by an improved recurrent neural network, the reasoning entity is obtained by calculating the correlation score with the natural language. We tested the performance of the NLKGF model on two datasets requiring multi-hop reasoning. The experimental results show that NLKGF beats advanced benchmark models in multi-hop reasoning tasks, which proves superiority of our model.
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Lu, X., Zhao, F., Jin, H. (2022). Fusion of Natural Language and Knowledge Graph for Multi-hop Reasoning. In: Zhao, X., Yang, S., Wang, X., Li, J. (eds) Web Information Systems and Applications. WISA 2022. Lecture Notes in Computer Science, vol 13579. Springer, Cham. https://doi.org/10.1007/978-3-031-20309-1_3
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