Discovering and Reconciling Semantic Conflicts: A Data Mining Perspective
Hongjun Lü, Weiguo Fan, Cheng Hian Goh, Stuart Madnick, David Wai-lok Cheung · 1998
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 dumber 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.