基于语义主题树的搜索覆盖度层状拓展方法

Search Coverage Degree Layered Expand Method Based on Semantic Topic

为了提高用户对网站使用的效率,提高网站本体模型的搜索性能,研究一种高效的网页语义概念树构建方法,进行搜索覆盖度层状拓展。传统方法中,使用搜索引擎的词语相似度算法进行搜索拓展,利用规则、聚类等技术对形式背景进行约简,无法有效简历概念间的上下位关系,性能不好。提出一种基于语义主题树特征匹配的搜索覆盖度层状拓展方法,进行Web语义模型和主题树构建,构建特征空间互信息区域文档词频向量模型,对数据库中记录的属性字段进行归类抽象,形成概念汇聚点,实现语义主题树构建搜索覆盖度拓展设计,构建语义主题树特征匹配算法,优化搜索引擎对文本特征的搜索敏感度,提高搜索覆盖度,实现文本搜索覆盖度层状拓展。实验分析得出,该方法具有较好的文本特征分类结果,语义层次结构清晰,可以有效提高文本数据召回率和查准率,展示了较好的应用价值。

In order to improve the efficiency of the use of web user, improving the performance of search web ontology model, method of constructing an effective Webpage semantic concept tree, expand the coverage of layered search. In traditional methods, the use of the word similarity algorithm of search engine search expansion, reduction of formal context by using the rules, clustering technology, it can effectively resume between concepts hyponymy, so the performance is not good. A search coverage degree layered expand method is proposed based on semantic topic characteristics matching, Web semantic model and the subject tree construction is obtained, mutual information of regional document word frequency vector model to construct the feature space to classify, abstract property field records in the database, forming the concept of rendezvous point, to realize the semantic topic tree constructed search coverage expansion design, the construction of the theme semantic tree matching algorithm, search engine optimization of text feature search sensitivity, increase the search coverage,realize the text search coverage layered development. The experimental analysis, the method has good classification results of text feature, semantic hierarchy is clear, it can effectively improve the recall and precision of text data, it shows a good application value.