KNOWLEDGE-BASED INTEGRATION OF HETEROGENEOUS DATABASES

We present an approach to integrate heterogeneous database schemas utilizing fuzzy real world knowledge. We model world knowledge by means of a fuzzy terminological network. On this basis we enrich classes semantically by determining the best tree spanning the class-name, its attributes, and its relationships in the network. The ambiguous edges of these trees are accumulated by means of fuzzy set intersection, and the trees can be used as a skeleton for further disambiguation. By unifying the spanning trees for two heterogeneous class definitions we can measure their degree of semantic resemblance. For resembling classes the unified tree(s) can then be used as a skeleton for proposing the most likely way(s) of their integration. Our approach specifically takes into account ambiguity arising from generic or polysemic names and from multiple possible roles of attributes and relationships. Furthermore, it allows for identification and integration of classes which maintain complementary rather than overlapping aspects of real world objects.