Semantic Web Dog Food

Presented at: 4th European Semantic Web Conference (ESWC2007)

by Eyal Oren, Sebastian Gerke, Stefan Decker

When creating Semantic Web data, users have to make a critical choice for a vocabulary: only through shared vocabularies can meaning be established. A centralised policy prevents terminology divergence but would restrict users needlessly. As seen in collaborative tagging environments, suggestion mechanisms help terminology convergerce without forcing users. We introduce two domain-independent algorithms for recommending predicates (RDF statements) about resources, based on statistical dataset analysis. The first algorithm is based on similarity between resources, the second one is based on co-occurrence of predicates. Experimental evaluation shows very promising results: a high precision with relatively high recall in linear runtime performance.

Keywords: annotation suggestion, recommender systems, semantic wiki, shared vocabularies, statistical reasoning

Resource URI on the dog food server: http://data.semanticweb.org/conference/eswc/2007/paper-279

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