An empirical study of instance-based ontology matching
A. Isaac, L. der Meij, S. Schlobach, and S. Wang. Proceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Korea, volume 4825 of LNCS, page 252--266. Berlin, Heidelberg, Springer Verlag, (November 2007)
Abstract
Instance-based ontology mapping is a promising family of solutions to a class of ontology alignment problems. Instance-based ontology mapping crucially depends on measuring the similarity between sets of annotated instances. In this paper we study how the choice of co-occurrence measures affects the performance of instance-based mapping. To this end, we have implemented a number of different statistical co-occurrence measures. We have prepared an extensive test case using vocabularies of thousands of terms, millions of instances, and hundreds of thousands of co-annotated items, and we have obtained a human Gold Standard judgement for part of the mapping-space. We then study how the different co-occurrence measures and a number of algorithmic variations perform on our benchmark dataset, as compared against the GoldStandard. Our systematic study shows excellent results of instance-based matching in general, where the more simple measures often outperform more sophisticated statistical co-occurrence measures.
%0 Conference Paper
%1 Isaac/2007/empirical
%A Isaac, Antoine
%A der Meij, Lourens Van
%A Schlobach, Stefan
%A Wang, Shenghui
%B Proceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Korea
%C Berlin, Heidelberg
%D 2007
%E Aberer, Karl
%E Choi, Key-Sun
%E Noy, Natasha
%E Allemang, Dean
%E Lee, Kyung-Il
%E Nixon, Lyndon J B
%E Golbeck, Jennifer
%E Mika, Peter
%E Maynard, Diana
%E Schreiber, Guus
%E Cudré-Mauroux, Philippe
%I Springer Verlag
%K 2007 iswc matching ontology research_12 study
%P 252--266
%T An empirical study of instance-based ontology matching
%U http://iswc2007.semanticweb.org/papers/253.pdf
%V 4825
%X Instance-based ontology mapping is a promising family of solutions to a class of ontology alignment problems. Instance-based ontology mapping crucially depends on measuring the similarity between sets of annotated instances. In this paper we study how the choice of co-occurrence measures affects the performance of instance-based mapping. To this end, we have implemented a number of different statistical co-occurrence measures. We have prepared an extensive test case using vocabularies of thousands of terms, millions of instances, and hundreds of thousands of co-annotated items, and we have obtained a human Gold Standard judgement for part of the mapping-space. We then study how the different co-occurrence measures and a number of algorithmic variations perform on our benchmark dataset, as compared against the GoldStandard. Our systematic study shows excellent results of instance-based matching in general, where the more simple measures often outperform more sophisticated statistical co-occurrence measures.
@inproceedings{Isaac/2007/empirical,
abstract = {Instance-based ontology mapping is a promising family of solutions to a class of ontology alignment problems. Instance-based ontology mapping crucially depends on measuring the similarity between sets of annotated instances. In this paper we study how the choice of co-occurrence measures affects the performance of instance-based mapping. To this end, we have implemented a number of different statistical co-occurrence measures. We have prepared an extensive test case using vocabularies of thousands of terms, millions of instances, and hundreds of thousands of co-annotated items, and we have obtained a human Gold Standard judgement for part of the mapping-space. We then study how the different co-occurrence measures and a number of algorithmic variations perform on our benchmark dataset, as compared against the GoldStandard. Our systematic study shows excellent results of instance-based matching in general, where the more simple measures often outperform more sophisticated statistical co-occurrence measures.},
added-at = {2007-11-07T19:13:58.000+0100},
address = {Berlin, Heidelberg},
author = {Isaac, Antoine and der Meij, Lourens Van and Schlobach, Stefan and Wang, Shenghui},
biburl = {https://www.bibsonomy.org/bibtex/2dc0f2e92eb694f1833568feb66218641/iswc2007},
booktitle = {Proceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Korea},
crossref = {http://data.semanticweb.org/conference/iswc-aswc/2007/proceedings},
editor = {Aberer, Karl and Choi, Key-Sun and Noy, Natasha and Allemang, Dean and Lee, Kyung-Il and Nixon, Lyndon J B and Golbeck, Jennifer and Mika, Peter and Maynard, Diana and Schreiber, Guus and Cudré-Mauroux, Philippe},
interhash = {1b8d31d4f03c0f965632cb93d9f48d5b},
intrahash = {dc0f2e92eb694f1833568feb66218641},
keywords = {2007 iswc matching ontology research_12 study},
month = {November},
pages = {252--266},
publisher = {Springer Verlag},
series = {LNCS},
timestamp = {2007-11-07T19:20:51.000+0100},
title = {An empirical study of instance-based ontology matching},
url = {http://iswc2007.semanticweb.org/papers/253.pdf},
volume = 4825,
year = 2007
}