基于二分图的RDF关键词扩展查询方法

Keyword Expansion Query Approach over RDF Data Based on Bipartite Graph

使用图表示RDF数据可以保持数据间的关联信息和语义信息,越来越多的关键词查询方法基于图结构实现RDF数据的查询处理。将二分图与RDF数据图相结合,定义RDF二分图模型,并提出一种基于二分图的RDF关键词扩展查询方法 KERBG。该方法将文本信息封装在二分图顶点标签上,以支持对关系的查询;利用关键词同义词扩展技术对查询关键词进行语义扩展,有效解决同一对象的描述用词的多样性问题,进而提高查准率;利用RDF二分图的反对称邻接矩阵及其幂矩阵构造包含关键顶点的查询结果子图,实现关键词查询处理,并降低查询响应时间。实验结果表明,在查准率和查询响应时间方面,提出的KERBG方法优于当前主流方法。

Using graph to express RDF data can both retain data correlation information and semantic information.To date,more and more keyword query methods based on graph structure have realized RDF data query processing.In this paper,an approach named RDF keyword expansion query approach based on bipartite graph was proposed.This approach enables keyword-based query over RDF data.RDF data is modeled as a RDF bipartite graph,in which all text information is encapsulated by nodes labels.Based on the keyword synonym expansion technology,the approach realizes the semantic extension of query keywords,effectively solves the problem of delivering the same object description words and also improves the query precision.Through RDF bipartite graph of the anti-symmetric adjacency matrix and its power matrix,the approach structures the subgraphs of query results consisting of key vertices,realizes the keyword query processing and then reduces the query response time.The experimental results show that when comparing query precision and query response time,KERBG method proposed in this paper is better than the current mainstream methods.