Improve the effectiveness of keyword search over relational database by node-temperature-based ant colony optimization

Keyword search over relational database has been researched a lot. By using simple keywords to search over relational data, ordinary users are not required to learn the difficult structural query language, thus resulting in better user friendliness. Search effectiveness is an important consideration for those solutions to this problem. The available methods adopt static ranking mechanism to ensure that the most relevant answer will be presented first to users. However, they are not able to dynamically optimize the search results according to the time-changing user interest. Here an ant-colony-optimizaton-based algorithm, called ACOKS, is proposed to deal with keyword search problem, in which node-temperature-based optimization is used to achieve dynamic search result optimization by following the track of user behavior. Extensive experimental results show that our methods can achieve better performance than the state-of-the-art methods.