A Holistic Approach on Deep Web Schema Matching

Schema matching is a critical problem in Deep Web integration process. This paper introduces a holistic approach, to match many schemas at the same time and find all matchings at once. We mainly analyses and compares the two existent archetypal systems: MGS and DCM. Furthermore, propose a new algorithm, named Correlated-clustering, based on advantages of the two existent systems. This algorithm first mines group attributes by positively correlated attributes, and then clusters the concepts by calculating the similarity of each two concepts, finally, develop a strategy to select matching. The experiment result shows the effectiveness and completeness of our algorithm, which demonstrate the promise of holistic schema matching.