@inproceedings{Isaac/2007/empirical,
	title = {An empirical study of instance-based ontology matching},
	address = {Berlin, Heidelberg},
	author = {Antoine Isaac and Lourens Van der Meij and Stefan Schlobach and Shenghui Wang},
	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 = {Karl Aberer and Key-Sun Choi and Natasha Noy and Dean Allemang and Kyung-Il Lee and Lyndon J B Nixon and Jennifer Golbeck and Peter Mika and Diana Maynard and Guus Schreiber and Philippe Cudré-Mauroux},
	month = {November},
	pages = {252--266},
	publisher = {Springer Verlag},
	series = {LNCS},
	url = {http://iswc2007.semanticweb.org/papers/253.pdf},
	volume = {4825},
	year = {2007},
	biburl = {http://www.bibsonomy.org/bibtex/2dc0f2e92eb694f1833568feb66218641/iswc2007},
	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.},
	keywords = {2007 iswc matching ontology research_12 study }
}