Matching Linked Open Data Entities to Local Thesaurus Concepts

We describe a solution for matching Linked Open Data (LOD) entities to concepts within a local thesaurus. The solution is currently integrated into a demonstrator of the PoolParty thesaurus management software. The underlying motivation is to support thesaurus users in linking locally relevant concepts in a thesaurus to descriptions available openly on the Web. Our concept matching algorithm ranks a list of potentially matching LOD entities with respect to a local thesaurus concept, based on their similarity. This similarity is calculated through string matching algorithms based not only on concept and entity labels, but also on the “context” of concepts, i.e. the values of properties of the local concept and the LOD concept. We evaluate over 41 different similarity algorithms on two test-ontologies with 17 and 50 concepts, respectively. The results of the first evaluation are validated on the second test-dataset of 50 concepts in order to ensure the generalisability of our chosen similarity matches. Finally, the overlap-, TFIDFand SoftTFIDF-similarity algorithms emerge as winners of this selection and evaluation procedure.