文章基本信息

标题：Concept Tree Based Information Retrieval Model
本地全文：下载
作者：Yuan, Chunyan
期刊名称：Journal of Multimedia
印刷版ISSN：1796-2048
出版年度：2014
卷号：9
期号：5
页码：652-659
DOI：10.4304/jmm.9.5.652-659
语种：English
出版社：Academy Publisher
摘要：This paper proposes a novel concept-based query expansion technique named Markov concept tree model (MCTM), discovering term relationship through the concept tree deduced by term markov network. We address two important issues for query expansion: the selection and the weighting of expansion search terms. In contrast to earlier methods, queries are expanded by adding those terms that are most similar to the concept of the query, rather than selecting terms that are similar to a signal query terms. Utilizing Markov network which is constructed according to the co-occurrence information of the terms in collection, it generate concept tree for each original query term, remove the redundant and irrelevant nodes in concept tree, then adjust the weight of original query and the weight of expansion term based on a pruning algorithm. We use this model for query expansion and evaluate the effectiveness of the model by examining the accuracy and robustness of the expansion methods, Compared with the baseline model, the experiments on standard dataset reveal that this method can achieve a better query quality
关键词：Concept Tree;Markov Networks;Query Expansion;Information Retrieval