| Citation: | LI X Y，FANG Q，Hu J，et al. Multi-hop knowledge graph question answering based on deformed graph matching[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（2）：529-534 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0375
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Knowledge Graph Question Answering (KGQA) is a process in which a given natural language question is semantically understood and parsed, and then the knowledge graph is used to query and reason to get the answer. But knowledge graphs which lack links, bring many challenges to multi-hop question answering. Many methods ignore important path information to evaluate the correlation between paths and multi-relationship problems when using knowledge graph embeddings, and text corpora also limit the scalability of text-enhanced models. Due to the drawbacks of these existing approaches, the Multi-hop Knowledge Graph Question Answering Based on Deformed Graph Matching (DGM-KGQA) method is proposed. This method builds semantic subgraphs using both question and topic entities, which then match the local structure of the knowledge graph to determine the correct solution. The experimental results on the benchmark dataset MetaQA verify the effectiveness of DGM-KGQA. In comparison to PullNet and EmbedKGQA, the accuracy of the answers retrieved on the completed knowledge graph is 4.2% higher than that of PullNet and 0.8% higher than that of EmbedKGQA. The accuracy of the answers retrieved on half of the knowledge graphs is 11.1% higher than that of PullNet and 0.5% higher than that of EmbedKGQA. Experiments show that the proposed deformed graph matching model can effectively enhance the relevance of knowledge graphs and the answer accuracy of multi-hop question answering.
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