Merging Interface Schemas on the Deep Web via Clustering Aggregation Wensheng Wu, AnHai Doan, and Clement T. Yu   View Paper (PDF)   Return to Session 17: Data Mining Applications Abstract We consider the problem of integrating a large number of interface schemas over the Deep Web, The scale of the problem and the diversity of the sources present serious challenges to the conventional manual or rule-based approaches to schema integration. To address these challenges, we propose a novel formulation of schema integration as an optimization problem, with the objective of maximally satisfying the constraints given by individual schemas. Since the optimization problem can be shown to be NP-complete, we develop a novel approximation algorithm LMax, which builds the unified schema via recursive applications of clustering aggregation. We further extend LMax to handle the irregularities frequently occurring among the interface schemas. Extensive evaluation on real-world data sets shows the effectiveness of our approach. ©2006 Association for Computing Machinery