Diego Ardila, José Abasolo, Fernando Lozano


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

author={Diego Ardila and José Abasolo and Fernando Lozano},
booktitle={Proceedings of the International Conference on Knowledge Engineering and Ontology Development - Volume 1: KEOD, (IC3K 2010)},

in EndNote Style

JO - Proceedings of the International Conference on Knowledge Engineering and Ontology Development - Volume 1: KEOD, (IC3K 2010)
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