关键词: XML伪反馈, 检索结果聚类, 排序, 查询扩展

Abstract: Pseudo relevance feedback (PRF) has been perceived as an effective solution for automatic query expansion. However, traditional pseudo relevance feedback can result in the query representation “drifting” away from the original query and a decreased retrieval performance. Therefore, the key issues in applying PRF are to identify the real relevant documents in the top retrieved results without any other assistant information, and expend the query based on the these relevant documents. This paper presents a solution framework from extensible markup language (XML) data. Firstly, this paper considers the XML content and structure features, and proposes a good XML query scheme based on pseudo relevance feedback documents by combining search results clustering with a two-stage ranking model. Furthermore, this paper explores the XML query expansion of CO (content only) query, and gives the term weight computation with structure. The experimental results show that the proposed scheme can reduce the topic drift effectively and obtain the better retrieval quality.

Key words: XML pseudo relevance feedback, search results clustering, ranking, query expansion