Semantic Web Dog Food

Presented at: 6th International and 2nd Asian Semantic Web Conference (ISWC2007+ASWC2007)

by Antoine Isaac, Lourens Van der Meij, Stefan Schlobach, Shenghui Wang

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.

An empirical study of instance-based ontology matching was presented at this event.

Resource URI on the dog food server: http://data.semanticweb.org/conference/iswc-aswc/2007/tracks/research/papers/253
Same as: http://revyu.com/things/iswc-aswc-2007-research-paper-253-an-empirical

Explore this resource elsewhere:

• on Snorql
• on Sindice
• on dotAC Explorer