基于潜在语义分析的Deep Web查询接口聚类研究

Research on Deep Web Query Interface Clustering Based on Latent Semantic Analysis

集成查询接口的生成是Deep Web数据集成的重要组成环节。如何对不同领域的查询接口进行有效的聚类是生成集成查询接口时需要解决的核心问题之一。针对传统的向量空间模型在Deep Web查询接口聚类时单纯依赖关键词匹配的缺点,引入潜在语义分析(LSA)的方法来发掘查询接口之间的语义关系,并给出了基于潜在语义分析的Deep Web查询接口聚类算法,最后采用UIUC的Web集成资源库提供的数据进行了实验。结果表明,潜在语义分析的方法提高了同一领域查询接口之间的相似度,明显改善了Deep Web查询接口聚类的质量。

Generation of integrated query interfaces is the important issue of Deep Web data integration. How to cluster different query interfaces effectively is one of the most core issues when generating integrated query interface. Due to the traditional vector space model can＇t solve the shortage of relying on keyword maching in the Deep Web query inter- face clustering, the Latent Semantic Analysis （LSA） method was introduced and then the algorithm of Deep Web query interface clustering based on Latent Semantic Analysis was proposed. The experimental results on UIUC Web integra- tion repository show that LSA method can significantly improve the performance of Deep Web query interface clustering.