基于关键词的RDF数据图查询模型研究  

Research of Keyword Search Model over RDF Data Graph

作　　者：郑志蕴[1] 刘博[1] 李伦[1] 王振飞[1] 

机构地区：[1]郑州大学信息工程学院,郑州450001

出　　处：《计算机科学》2015年第7期234-239,249,共7页Computer Science

基　　金：河南省国际科技合作项目(144300510007)资助

摘　　要：随着语义网数据的海量涌现,人们更加关注RDF图的数据查询效率,通过关键词匹配直接查询RDF数据图成为一个研究热点。针对关键词查询中普遍存在的结果冗余与偏离等问题,提出了一种基于关键词的RDF数据图查询模型。该模型首先采用提出的基于迭代的图查询算法(ISGR)对所查询关键词进行子图匹配,得到唯一且最大的结果子图集合;然后根据关键词图与结果子图之间的结构信息,利用统计语言模型,给出了一种结果子图排序方法(SimLM)。对比实验表明,提出的查询模型及排序方法在一致性和相关性方面的性能优于传统模型。As huge amounts of the semantic Web data have sprung up,people are more concerned about query efficiency over RDF data graph.Retrieving RDF data graph directly by keyword matching is an area of research focus.In this paper,a retrieval model was proposed,which enables keyword search for RDF graph.First,for the improvement of query efficiency,an algorithm named ISGR（an Iterative way to SubGraph Retrieval）was proposed,in which query keywords can be matched with subgraphs from RDF data graph,and a collection of subgraphs which should be unique and maximal is got.Next,in order to solve the problems of redundant results and deviation that frequently emerge in keyword search,a mixture ranking model（SimLM）was proposed,which considers the structural information between keyword graph and result graph,and mixs statistical language model.A numbers of contrast experiments over two kinds of open source real datasets prove that the retrieval and ranking model proposed in this paper outperforms well-known techniques in the field of consistency and relevance.

关 键 词：RDF数据图 关键词查询 子图 相似度矩阵 统计语言模型 

分 类 号：TP391.3[自动化与计算机技术—计算机应用技术]