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[目的/意义]针对当前查询扩展技术面临的瓶颈,提出一种关联数据驱动的查询扩展方法,改善检索系统的查全率、查准率。[方法/过程]将扩散激活理论应用到关联数据集中,使得在输入查询词搜索潜在语义实体时,对提取的查询词的语义特征在知识库中进行有特定机制的扩散和激活,最后对这些语义关联的候补概念进行收集,并利用推理机制进行筛选,得到更优的概念集。[结果/结论]该方法能有效提高检索系统的查全率、查准率,证明了本文提出的技术的可行性、有效性。
[Purpose / Significance] Aiming at the bottleneck of the current query expansion technology, this paper proposes a correlation data-driven query expansion method to improve the recall and precision of the retrieval system. [Method / Procedure] The theory of diffusion activation is applied to related data sets so that the semantic features of the extracted query terms are diffused and activated in the knowledge base with specific mechanisms when the input query searches for potential semantic entities. Finally, Semantic association of candidate concepts collected, and the use of reasoning mechanism for screening, to get a better set of concepts. [Result / Conclusion] This method can effectively improve the recall rate and accuracy of the retrieval system, which proves the feasibility and effectiveness of the proposed technique.