A HYBRID METHOD FOR FUZZY ONTOLOGY INTEGRATION

The main contribution of this work consists of combining a heuristic method for propagation of matchable concepts and using consensus techniques for conflict resolution for fuzzy ontology integration. Two central observations behind this approach are as follows: (1) if two concepts across different source ontologies equivalently match each other, then their neighboring concepts will be often matched as well; and (2) conflicts regarding integration of multiple ontologies can be resolved by creating a consensus among the conflict ontological entities. The key idea of the first observation is to start from an aligned pair of concepts (called medoids) to determine so-called potentially common parts to provide additional suggestions for possible matching concepts. This approach is used to obtain pairs of matchable concepts and to avoid pairs of mismatching concepts. On the other hand, the second observation is used to discover a new merged concept from matched concepts by making a consensus among conflict ontological entities. This idea is to determine the best representative as the merged version of the component ones. A combination of both observations for fuzzy ontology integration is a significant contribution of this work. The results of the experiments imply that the proposed approach is effective with regard to both completeness and accuracy.