%0 Book Section %1 citeulike:4039753 %A Isaac, Antoine %A van der Meij, Lourens %A Schlobach, Stefan %A Wang, Shenghui %D 2008 %J The Semantic Web %K aswc07, iswc07 %P 253--266 %R http://dx.doi.org/10.1007/978-3-540-76298-0\_19 %T An Empirical Study of Instance-Based Ontology Matching %U http://dx.doi.org/10.1007/978-3-540-76298-0\_19 %X Instance-based ontology mapping is a promising family of solutions to a class of ontology alignment problems. It 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. 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 Gold Standard. 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.

@incollection{citeulike:4039753, abstract = {Instance-based ontology mapping is a promising family of solutions to a class of ontology alignment problems. It 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. 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 Gold Standard. 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 = {2009-02-13T13:22:04.000+0100}, author = {Isaac, Antoine and van der Meij, Lourens and Schlobach, Stefan and Wang, Shenghui}, biburl = {https://www.bibsonomy.org/bibtex/2a9089fb09bdd4efa8898158ea6951217/conchuir}, citeulike-article-id = {4039753}, doi = {http://dx.doi.org/10.1007/978-3-540-76298-0\_19}, interhash = {45179a67762d525b6cb36364e20f7da0}, intrahash = {a9089fb09bdd4efa8898158ea6951217}, journal = {The Semantic Web}, keywords = {aswc07, iswc07}, pages = {253--266}, posted-at = {2009-02-12 18:11:48}, priority = {2}, timestamp = {2009-02-13T13:22:05.000+0100}, title = {An Empirical Study of Instance-Based Ontology Matching}, url = {http://dx.doi.org/10.1007/978-3-540-76298-0\_19}, year = 2008 }