Schema Evolution publication categorizer

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.