Discovering and Reconciling Semantic Conflicts: A Data Mining Perspective

摘要

Current approaches to semantic interoperability require human intervention in detecting potential conflicts and in defining how those conflicts may be resolved. This is a major impedance to achieving "logical connectivity", especially when the number of disparate sources is large. In this paper, we demonstrate that the detection and reconciliation of semantic conflicts can be automated using tools and techniques developed by the data mining community. We describe a process for discovering such rules that represent the relationships among semanticaly related attributes and illustrate the effectiveness of our approach with examples.