Schema matching based on Ant Colony Optimization

Schema matching is widely used in many database applications, such as ontology merging, data integration, data warehouse and dataspaces. The problem of schema matching is essentially to find the semantic correspondences between attributes of schemas to be matched. The query logs imply lots of valuable information about the schemas to be matched. Thus, we propose to employ Ant Colony Optimization (ACO) to solve the problem of schema matching based on the information implied in query logs. Our main idea is to firstly extract the features about schemas from the query logs, then, use the scoring function to measure the similarity of these features, finally employ the ant colony optimization to find the optimal matching result. We validate our approach with an experimental study, the results of which demonstrate that our approach is effective and has good performance.