Formulation Schema Matching Problem for Combinatorial Optimization Problem

Schema matching is the task of finding semantic correspondences between elements of two schemas, which plays a key role in many database applications. In this paper, we cast the schema matching problem (SMP) into a multi-labeled graph matching problem. First, we propose an internal schema model: multi-labeled graph model, and transform schemas into multi-labeled graphs. Therefore, SMP reduce to a labeled graph matching, which is a classic combinatorial problem. Secondly, we study a generic graph similarity measure based on Contrast Model, and propose a versatile optimization function to compare two multi-labeled graphs. Then, we can design the optimization algorithm to solve the multi-labeled graph matching problem. Based on the matching result obtained by greedy matching, we implement a fast hybrid search algorithm to find the feasible matching results. Finally, we use several schemas to test the hybrid search algorithm. The experimental results confirm that the algorithm model and the hybrid algorithm are effective.