MULTIPLE KERNEL LEARNING FOR ONTOLOGY INSTANCE MATCHING

Diego Ardila, José Abasolo, Fernando Lozano

Abstract

This paper proposes to apply Multiple Kernel Learning and Indefinite Kernels (IK) to combine and tune Similarity Measures within the context of Ontology Instance Matching. We explain why MKL can be used in parameter selection and similarity measure combination; argue that IK theory is required in order to use MKL within this context; propose a configuration that makes use of both concepts; and present, using the IIMB bechmark, results of a prototype to show the feasibility of this idea in comparison with other matching tools.

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Paper Citation


in Harvard Style

Ardila D., Abasolo J. and Lozano F. (2010). MULTIPLE KERNEL LEARNING FOR ONTOLOGY INSTANCE MATCHING . In Proceedings of the International Conference on Knowledge Engineering and Ontology Development - Volume 1: KEOD, (IC3K 2010) ISBN 978-989-8425-29-4, pages 311-318. DOI: 10.5220/0003117403110318


in Bibtex Style

@conference{keod10,
author={Diego Ardila and José Abasolo and Fernando Lozano},
title={MULTIPLE KERNEL LEARNING FOR ONTOLOGY INSTANCE MATCHING},
booktitle={Proceedings of the International Conference on Knowledge Engineering and Ontology Development - Volume 1: KEOD, (IC3K 2010)},
year={2010},
pages={311-318},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003117403110318},
isbn={978-989-8425-29-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Engineering and Ontology Development - Volume 1: KEOD, (IC3K 2010)
TI - MULTIPLE KERNEL LEARNING FOR ONTOLOGY INSTANCE MATCHING
SN - 978-989-8425-29-4
AU - Ardila D.
AU - Abasolo J.
AU - Lozano F.
PY - 2010
SP - 311
EP - 318
DO - 10.5220/0003117403110318