A structure-based approach of keyword querying for fuzzy XML data

摘要

Keyword query on XML data has attracted many researchers’ attention. The existing keyword query methods on XML data are mainly based on the LCA (lowest common ancestor) semantics and its variants (SLCA, ELCA, et al.). These semantics are mainly focused on finding the results of “AND” semantics among keywords which makes the query results incomplete. The structure query language can return more meaningful and comprehensive answers, but it is difficult for a user without the knowledge of the structure and schema of an XML document to propose a structure query statement. In the reality, there exists plenty of uncertainty and ambiguity, and how to search the useful information on fuzzy XML data becomes an important research issue. In this paper, we introduce the structure query language into the keyword query in fuzzy XML data to get more comprehensive query results. First, we propose the concepts of object tree, the minimum object tree and the nearest object tree and propose a semantics of matching object trees for keyword query to capture the user’s query intention. Then, we give our query method AO-Twig to combine the structure query language with keyword query to obtain the Top-K query results with the highest scores. Finally, experimental results on both real datasets and synthetic datasets show that the proposed method AO-Twig performs well for finding Top-K results of keyword queries over fuzzy XML data.