An approach of top-k keyword querying for fuzzy XML

Keyword search on XML document has received wide attention. Many search semantics and algorithms have been proposed for XML keyword queries. But the existing approaches fall short in their abilities to support keyword queries over fuzzy XML documents. To overcome this limitation, in this paper, we discuss how to obtain and evaluate top-k smallest lowest common ancestor (SLCA) results of keyword queries on fuzzy XML documents. We define the fuzzy SLCA semantics on the fuzzy XML document, and then propose a novel encoding scheme to denote different types of nodes in fuzzy XML documents. After these, we propose two efficient algorithms to find k SLCA results with highest possibilities for a given keyword query on the fuzzy XML document. First one is an algorithm which can obtain the top-k SLCA results and their possibilities based on the stack technique. The second algorithm can obtain top-k SLCA results of keyword queries based on a set of SLCA's properties. Finally, we compare and evaluate the performances of the two algorithms.