Abstract: Based on the knowledge graph embedding model, we proposed a scoring method combining knowledge graph embedding scoring and link scoring to solve multi-hop knowledge graph question answering task in the Chinese domain, which had wider applicability compared with the traditional single-hop knowledge question answering methods. The method constructed a query link while searching for the optimal answer, and gave the answer set by query, which effectively alleviated the situation of missing answers in existing methods. The experimental results on the NLPCC-MH dataset show that the average F₁ value of the method on multi-hop problems is 0.653, which is significantly better than the comparison method. Real knowledge graphs usually have missing links, and the experiments simulate the sparsity of knowledge graphs by randomly discarding 25% triples, the results show that the method is still effective in this case.

Key words: knowledge graph, intelligent question answering, knowledge graph embedding, link prediction