What is it about?
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.
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Why is it important?
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.
Perspectives
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
Dr Jorge Martinez-Gil
Software Competence Center Hagenberg GmbH
Read the Original
This page is a summary of: Evaluation of two heuristic approaches to solve the ontology meta-matching problem, Knowledge and Information Systems, December 2009, Springer Science + Business Media,
DOI: 10.1007/s10115-009-0277-0.
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Resources
Evaluation of two heuristic approaches to solve the ontology meta-matching problem
Evaluation of two heuristic approaches to solve the ontology meta-matching problem - Preprint
Evaluation of two heuristic approaches to solve the ontology meta-matching problem
Evaluation of two heuristic approaches to solve the ontology meta-matching problem - Preprint
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