Ontology-Matching-Heuristic.pdf (757.91 kB)
Evaluation of Two Heuristic Approaches to Solve the Ontology Meta-Matching Problem
Nowadays many techniques and tools are available for addressing the
ontology matching problem, however, the complex nature of this problem
causes existing solutions to be unsatisfactory. This work aims to shed
some light on a more flexible way of matching ontologies. Ontology
meta-matching, which is a set of techniques to configure optimum
ontology matching functions. In this sense, we propose two approaches to
automatically solve the ontology meta-matching problem. The first one
is called maximum similarity measure, which is based on a greedy
strategy to compute efficiently the parameters which configure a
composite matching algorithm. The second approach is called genetics for
ontology alignments and is based on a genetic algorithm which scales
better for a large number of atomic matching algorithms in the composite
algorithm and is able to optimize the results of the matching process.