| Abstract |
Finding mappings between compatible ontologies is an important but
difficult open problem. Instance-based methods for solving this
problem have the advantage of focusing on the most active parts of
the ontologies and reflect concept semantics as they are actually
being used. However such methods have not at present been widely
investigated in ontology mapping, compared to linguistic and
structural techniques. Furthermore, previous instance-based mapping
techniques were only applicable to cases where a substantial set of
instances was available that was doubly annotated with both
vocabularies. In this paper we approach the mapping problem as a
classification problem based on the similarity between instances of
concepts. This has the advantage that no doubly annotated instances
are required, so that the method can be applied to any two corpora
annotated with their own vocabularies. We evaluate the resulting
classifiers on two real-world use cases, one with homogeneous and
one with heterogeneous instances. The results illustrate the
efficiency and generality of this method.
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