Comparing Vocabulary Term Recommendations using Association Rules and Learning To Rank: A User Study

DOI

The user-study evaluates a vocabulary term recommendation service that is based on how other data providers have used RDF classes and properties in the Linked Open Data cloud. The 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. This data collection comprises the raw results of this user-study in SPSS format.

Identifier
DOI https://doi.org/10.7802/1206
Metadata Access https://api.datacite.org/dois/10.7802/1206
Provenance
Creator Schaible, Johann; Szekely, Pedro; Scherp, Ansgar
Publisher GESIS Data Archive
Publication Year 2016
OpenAccess true
Representation
Resource Type Dataset
Format application/pdf; application/x-spss-sav
Size 450167; 37398
Version 1
Discipline Social Sciences
Spatial Coverage Deutschland / DE; Vereinigte Staaten / US; Germany / DE; United States / US