Abstract：In multi-hop inference models, path information is formed by fully mining and utilizing multi-step relationships between entities in the knowledge graph to accomplish knowledge inference. To solve the problems of sparse data and low reliability of inference paths in most of the existing sparse knowledge graph multi-hop inference models, a multi-hop inference model for knowledge graphs incorporating semantic information is proposed. Firstly, entities and relations in the knowledge graph are embedded into the vector space as the external environment for reinforcement learning training. Then, the semantic information of query relations and inference paths is employed to select the (relation, entity) pair with the highest similarity to expand the action space for path search by the agent, and thus the lack of sparse data in the inference process is compensated. Finally, the semantic similarity between the inference path and the query relation is utilized to evaluate the reliability of the inference path and it is fed back to the agent as a reward function. Experiments on several publicly available sparse datasets show that the inference performance of the proposed model is significantly improved.
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