@inProceedings{
 title = {An empirical study of instance-based ontology matching},
 type = {inProceedings},
 year = {2007},
 identifiers = {[object Object]},
 pages = {253-266},
 volume = {4825},
 websites = {http://www.springerlink.com/index/7435643j17168414.pdf},
 publisher = {Springer},
 series = {Lecture Notes in Computer Science},
 editors = {[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]},
 id = {0ebf6f25-0788-38dd-9aee-a1848dbab3c5},
 created = {2015-05-16T16:30:53.000Z},
 file_attached = {false},
 profile_id = {0c4c49d2-3b4e-3d8d-9ea8-7bab2d49bb47},
 group_id = {7fc8fad4-ddf6-3b48-b39c-c05d75ba7135},
 last_modified = {2017-03-14T10:16:57.191Z},
 read = {false},
 starred = {false},
 authored = {false},
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 citation_key = {bnaic08},
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 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.},
 bibtype = {inProceedings},
 author = {Isaac, Antoine and van der Meij, Lourens and Schlobach, Stefan and Wang, Shenghui},
 booktitle = {Proceedings of the 20th Belgium-Netherlands Conference on Artificial Intelligence (BNAIC 2008)}
}

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