关键词: 局部反馈, 查询扩展, 关联规则, 负关联规则

Abstract: In order to differentiate from positive and negative expansion terms related to original query and enhance query expansion performance, a novel query expansion algorithm of local feedback is proposed based on association rules [q→ti] and [q→?tj], which applies positive and negative association rules mining technique to query expansion. Those positive and negative association rules [q→ti] and [q→?tj] only containing original query terms are automatically mined from the top-ranked retrieved documents to construct positive and negative association rules database respectively. Positive and negative expansion terms related to original query are extracted from these databases to build positive and negative expansion terms database separately. The terms the same as negative expansion terms are removed from positive expansion terms database and the rest of the terms of the positive expansion terms database are combined with original query for query expansion. A new query expansion model and computing method for weights of expansion terms are presented, which make the weighted value of an expansion term more reasonable. The results of the experiment show that the algorithm proposed can not only detect those false expansion terms but also improve and enhance the information retrieval performance.

Key words: local feedback, query expansion, association rules, negative association rules