Abstract: Aiming at the limitations of existing scientific document query expansion methods,such as insufficient utilization of document information and failure to effectively exploit inter-document relationships,a scientific document query expansion method based on multi-dimensional meta-path in the know-ledge graph is proposed.Firstly,the pseudo-relevant feedback document set is processed to obtain a candidate expansion term set.Then,based on the analysis of the scientific document knowledge graph,appropriate meta-paths are identified to represent the relationships between user queries and candidate expansion terms,and multi-dimensional semantic relevance scores between them are calculated based on different meta-path associations between nodes.Finally,the multi-dimensional semantic relevance scores and the weights of candidate expansion terms in the pseudo-relevant feedback document set are fused to select the final expansion terms,thereby achieving query expansion.Experimental results show that compared with existing query expansion methods,the proposed method improves mAP,DCG,and NDCG by at least 9.21%,10%,and 11.7%,respectively.

Key words: knowledge graph, query expansion, multi-dimensional meta-path, scientific document, information retrieval