  Ontology matching is the process of automatically determining the semantic
equivalences between the concepts of two ontologies. Most ontology matching
algorithms are based on two types of strategies: terminology-based strategies,
which align concepts based on their names or descriptions, and structure-based
strategies, which exploit concept hierarchies to find the alignment. In many
domains, there is additional information about the relationships of concepts
represented in various ways, such as Bayesian networks, decision trees, and
association rules. We propose to use the similarities between these
relationships to find more accurate alignments. We accomplish this by defining
soft constraints that prefer alignments where corresponding concepts have the
same local relationships encoded as knowledge rules. We use a probabilistic
framework to integrate this new knowledge-based strategy with standard
terminology-based and structure-based strategies. Furthermore, our method is
particularly effective in identifying correspondences between complex concepts.
Our method achieves substantially better F-score than the previous
state-of-the-art on three ontology matching domains.
