基于信息网模型的Web实体语义信息搜索平台

Search Platform for Web Entity Semantic Information Based on Information Network Model

Web上实体信息过于分散且缺乏语义,传统基于关键词匹配的搜索引擎往往因缺少上下文等语义信息,无法搜索到精确的结果。为了对Web数据进行精确查找,使用信息网模型(INM)对Web数据进行语义表示和建模,将实体的所有语义信息组织在一个对象中,快速获取实体完整的语义信息。基于INM构建复杂语义数据库,设计实现一个可对教育领域相关实体信息进行精确搜索的TLDW系统。实验结果表明,该系统初次查询时间均在100 ms内,其搜索结果包含实体的上下文关系等多种语义信息,缓存优化后的搜索结果可在20 ms内完成。

The information of Web entity is scattered in different places and lack of semantics. Traditional search engines based on keyword matching cannot get accurate results for lacking of context information. To get accurate information on the Web,Information Networking Model（INM） is used to directly describe relationships between entities in the real world, and to represent and model the semantic information of Web data. The semantic information about one entity is organized into an object, which makes it more quickly to acquire complete entity information. Based on the semantic database generated with INM, a semantic information search system named TLDW is built, which can be used to search semantic information of entities in the education domain. Experimental results show that first task searching time is less than 100 ms,and search results contain rich entity semantic information like context relationship. On the basis of the cache optimization, searching task can be finished within 20 ms.