【摘要】 针对现有科技文档的查询扩展方法存在文档信息利用不充分、文档间关联关系未能有效利用等方面的局限性，提出一种基于知识图谱中多维元路径的科技文档查询扩展方法。首先，对伪相关反馈文档集进行处理得到候选扩展词集；其次，在对科技文档知识图谱进行分析的基础上，寻找合适的元路径表示用户查询与候选扩展词的关联关系，并基于节点间不同的元路径关联计算用户查询与候选扩展词之间的多维语义相关度；最后，融合多维语义相关度以及候选扩展词在伪相关反馈文档集中的权重选择最终扩展词，实现对用户查询的扩展。实验结果显示，与已有的查询扩展方法相比，基于知识图谱中多维元路径的科技文档查询扩展方法在mAP,DCG和NDCG上分别至少提升了9.21%,10%和11.7%。更多还原

【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.更多还原