Comparing vocabulary term recommendations using association rules and learning to rank: A user study

Citation

Schaible J, Szekely P & Scherp A (2016) Comparing vocabulary term recommendations using association rules and learning to rank: A user study. In: Sack H, Blomqvist E, d'Aquin M, Ghidini C, Paolo Ponzetto S & Lange C (eds.) The Semantic Web. Latest Advances and New Domains. ESWC 2016, volume 9678. Lecture Notes in Computer Science, 9678. European Semantic Web Conference (ESWC) 2016, Crete, Greece, 29.05.2016-02.06.2016. Cham, Switzerland: Springer Verlag, pp. 214-230. https://doi.org/10.1007/978-3-319-34129-3_14

Abstract
When modeling Linked Open Data (LOD), reusing appropriate vocabulary terms to represent the data is difficult, because there are many vocabularies to choose from. Vocabulary term recommendations could alleviate this situation. We present a user study evaluating a vocabulary term recommendation service that is based on how other data providers have used RDF classes and properties in the LOD cloud. Our 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 in models of better quality.

Keywords
Association rule; resource description framework; user study; modeling task; association rule mining;

Journal
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Volume 9678