负关联规则挖掘与特征词抽取融合的局部反馈查询扩展

Query Expansion of Local Feedback Based on the Fusion of Negative Association Rules Mining and Feature Terms Extraction

针对现有信息检索系统中存在的词不匹配问题,本文提出一种基于负关联规则挖掘与特征词抽取融合的局部反馈查询扩展算法。该算法首先从前列n篇初检局部文档中抽取特征词,建立特征词库;然后,对特征词库挖掘同时含有查询词和非查询词的频繁项集和非频繁项集,由此挖掘前件是查询项的负关联规则,提取负关联规则的后件作为负关联特征词,计算负关联特征词与原查询的相关性,根据相关性在特征词库中删除负关联特征词,将余下的特征词作为最终扩展词,和原查询组合成新查询实现查询扩展。实验结果表明,该算法能有效地提高和改善信息检索性能。

Aiming at the term mismatch issues of the existing information retrieval systems, a novel query expansion algorithm of local feedback is proposed based on the fusion of negative association rules mining and feature terms extraction. Firstly, the feature terms from the top-ranked n retrieved local documents are extracted to construct the feature terms database, and the frequent itemsets and non-frequent itemsets containing original query terms and non query terms synchronously are mined in the feature terms database. And then, the negative association rules, the antecedent of which is the original query terms, are mined in frequent itemsets and non-frequent itemsets, and the consequents of the negative as sociation rules are extracted as the negative association terms, and the correlation of each negative association term and the entire original query is calculated. Finally, the terms which are the same as the negative association terms are removed from the feature terms database according to the correlation and the rest of the terms of the feature terms database are combined with the original query for query expansion. The results of the experiment show that the proposed algorithm can effectively improve and enhance the information retrieval performance.