Simple Algorithms for Predicate Suggestions using Similarity and Co-Occurrence
From semanticweb.org
A paper written by Stefan Decker, Eyal Oren and Sebastian Gerke. It was presented at the ESWC2007. It is about shared vocabularies, statistical reasoning, annotation suggestion, semantic wiki and recommender systems
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[edit] Abstract
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.
This data has been imported from the ESWC2007 RDF
