Abstract
Keyword search in relational databases allows the user to search information without knowing database schema and using structural query language. As results needed by user are assembled from connected tuples of multiple relations, ranking keyword queries are needed to retrieve relevant results. For a given keyword query, the authors first generate candidate networks and also produce connected tuple trees according to the generated candidate networks by reducing the size of intermediate joining results. They then model the generated connected tuple trees as a document and evaluate score for each document to estimate its relevance. Finally, the authors retrieve top-k keyword queries by ranking the results. In this paper, the authors propose a new ranking method based on virtual document. They also propose Top-k CTT algorithm by using the frequency threshold value. The experimental results are shown by comparison of the proposed ranking method and the previous ranking methods on IMDB and DBLP datasets.
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