Matches in ScholarlyData for { ?s ?p When modeling Linked Open Data (LOD), choosing appropriate vocabulary terms to represent data entities and relations between data entities is difficult, because there are many vocabularies to choose from. Inappropriate choices lead to LOD that is difficult both to understand for humans as well as to automatically exploit by machines. We present an evaluation of approaches that try to alleviate this situation by recommending vocabulary terms based on how other data providers have used RDF classes and properties in the LOD cloud. Our user study compares the machine learning technique Learning to Rank (L2R), the classical data mining approach Association Rule mining (AR), and a baseline that does not provide any recommendations. Results show that utilizing AR, participants needed less time and less effort to model the data, which in the end resulted to be of better quality.. }
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- 222 abstract "When modeling Linked Open Data (LOD), choosing appropriate vocabulary terms to represent data entities and relations between data entities is difficult, because there are many vocabularies to choose from. Inappropriate choices lead to LOD that is difficult both to understand for humans as well as to automatically exploit by machines. We present an evaluation of approaches that try to alleviate this situation by recommending vocabulary terms based on how other data providers have used RDF classes and properties in the LOD cloud. Our user study compares the machine learning technique Learning to Rank (L2R), the classical data mining approach Association Rule mining (AR), and a baseline that does not provide any recommendations. Results show that utilizing AR, participants needed less time and less effort to model the data, which in the end resulted to be of better quality.".