Discovering Executable Semantic Mappings Between Ontologies

Creating executable semantic mappings is an important task for ontology-based information integration. Although it is argued that mapping tools may require interaction from humans (domain experts) for best accuracy, in general, automatic ontology mapping is an AI-Complete problem. Finding matchings (correspondences) between the concepts of two ontologies is the first step towards solving this problem but matchings are normally not directly executable for data exchange or query translation. This paper presents an systematic approach to combining ontology matching, object reconciliation and multi-relational data mining to find the executable mapping rules in a highly automatic manner. Our approach starts from an iterative process to search the matchings and do object reconciliation for the ontologies with data instances. Then the result of this iterative process is used for mining frequent queries. Finally the semantic mapping rules can be generated from the frequent queries. The results show our approach is highly automatic without losing much accuracy compared with human-specified mappings.