Richly Semantical Keyword Searching over Relational Databases

With the development of keyword search over relational databases, how to improve the result quality is a popular problem. To solve it, existing work mainly are CN-based and graph-based. The CN-based approaches occupy little memory space and have high level of abstract. However, the defect of these approaches is not considering meaningful information in metadata of databases. In this paper, we propose a novel architecture. First, it provides richer semantics for keywords to generate more meaningful candidate networks. Second, it provides query templates to facilitate the query transforms of candidate networks, which contribute to generating more meaningful query results. Third, some properties of ranking are simply summarized. Finally, the experimental results demonstrate that the result quality is improved.