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- paper-279 type InProceedings.
- paper-279 label "Simple Algorithms for Predicate Suggestions using Similarity and Co-Occurrence".
- paper-279 sameAs paper-279.
- paper-279 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.".
- paper-279 hasAuthorList authorList.
- paper-279 keyword "annotation suggestion".
- paper-279 keyword "recommender systems".
- paper-279 keyword "semantic wiki".
- paper-279 keyword "shared vocabularies".
- paper-279 keyword "statistical reasoning".
- paper-279 title "Simple Algorithms for Predicate Suggestions using Similarity and Co-Occurrence".