J. Wang, Z. Ding, and C. Jiang. Proceedings of the IEEE Asia-Pacific Conference on Services Computing (APSCC), page 617--620. Washington, DC, USA, IEEE Computer Society, (2006)
Abstract
In this paper a genetic algorithm-based optimization procedure for ontology matching problem is presented as a feature-matching process. First, from a global view, we model the problem of ontology matching as an optimization problem of a mapping between two compared ontologies, and every ontology has its associated feature sets. Second, as a powerful heuristic search strategy, genetic algorithm is employed for the ontology matching problem. Given a certain mapping as optimizing object for GA, fitness function is defined as a global similarity measure function between two ontologies based on feature sets. Finally, a set of experiments are conducted to analysis and evaluate the performance of GA in solving ontology matching problem.
%0 Conference Paper
%1 Wang2006GAOntologyMatching
%A Wang, Junli
%A Ding, Zhijun
%A Jiang, Changjun
%B Proceedings of the IEEE Asia-Pacific Conference on Services Computing (APSCC)
%C Washington, DC, USA
%D 2006
%I IEEE Computer Society
%K knowledge matching measures semantic semantic-measures
%P 617--620
%T GAOM: Genetic Algorithm Based Ontology Matching
%U http://www.dit.unitn.it/~p2p/RelatedWork/Matching/04041300.pdf
%X In this paper a genetic algorithm-based optimization procedure for ontology matching problem is presented as a feature-matching process. First, from a global view, we model the problem of ontology matching as an optimization problem of a mapping between two compared ontologies, and every ontology has its associated feature sets. Second, as a powerful heuristic search strategy, genetic algorithm is employed for the ontology matching problem. Given a certain mapping as optimizing object for GA, fitness function is defined as a global similarity measure function between two ontologies based on feature sets. Finally, a set of experiments are conducted to analysis and evaluate the performance of GA in solving ontology matching problem.
%@ 0-7695-2751-5
@inproceedings{Wang2006GAOntologyMatching,
abstract = {In this paper a genetic algorithm-based optimization procedure for ontology matching problem is presented as a feature-matching process. First, from a global view, we model the problem of ontology matching as an optimization problem of a mapping between two compared ontologies, and every ontology has its associated feature sets. Second, as a powerful heuristic search strategy, genetic algorithm is employed for the ontology matching problem. Given a certain mapping as optimizing object for GA, fitness function is defined as a global similarity measure function between two ontologies based on feature sets. Finally, a set of experiments are conducted to analysis and evaluate the performance of GA in solving ontology matching problem.},
added-at = {2018-06-22T10:25:44.000+0200},
address = {Washington, DC, USA},
author = {Wang, Junli and Ding, Zhijun and Jiang, Changjun},
biburl = {https://www.bibsonomy.org/bibtex/29cf458de5b89006bda0c9b708ebc24e8/theodoro},
booktitle = {Proceedings of the IEEE Asia-Pacific Conference on Services Computing (APSCC)},
groups = {public},
interhash = {6c86d7adedb51626e65a88ece90004b4},
intrahash = {9cf458de5b89006bda0c9b708ebc24e8},
isbn = {0-7695-2751-5},
keywords = {knowledge matching measures semantic semantic-measures},
pages = {617--620},
publisher = {IEEE Computer Society},
timestamp = {2018-06-22T10:25:44.000+0200},
title = {{GAOM: Genetic Algorithm Based Ontology Matching}},
url = {http://www.dit.unitn.it/~p2p/RelatedWork/Matching/04041300.pdf},
username = {gergie},
year = 2006
}