As the wealth of structured repositories of educational content for agricultural object is increasing, the problem of heterogeneity between them on a semantic level is becoming more prominent. Ontology matching is a technique that helps to identify the correspondences on the description schemas of different sources and provide the basis for interesting applications that exploit the information in a linked fashion. The present paper presents a data-driven approach for discovering matches between different classification schemas. The approach is based on content analysis and linguistic processing in order to extract information in the form of relation tuples, use the extracted information to associate the content of different repositories and match their underlying classification schemas based on the degree of content similarity. The preliminary results verified the validity of the approach and exposed the need for refinements on the linguistic processing of the available textual information and on the definition of relation similarity.
Contributed by Antonis Koukourikos, Giannis Stoitsis, Pythagoras Karampiperis
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