Comparing Vocabulary Term Recommendations Using Association Rules and Learning to Rank: A User Study

  • Johann SchaibleEmail author
  • Pedro Szekely
  • Ansgar Scherp
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9678)


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.


Association Rule Resource Description Framework User Study Modeling Task Association Rule Mining 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We would like to thank Laura Hollink, Benjamin Zapilko, Ruben Verborgh, Jérôme Euzenat, and Oscar Corcho for providing the essential contribution to generate the gold standard RDF representation of the assignment datasets. We also thank Malte Knauf and Thomas Gottron for providing the basis of our Association Rule generation algorithm for calculating the vocabulary term recommendations. Naturally, we also thank the participants of the user study for helping us with our research.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.GESIS – Leibniz Institute for the Social SciencesCologneGermany
  2. 2.Information Sciences InstituteUniversity of Southern CaliforniaLos AngelesUSA
  3. 3.ZBW – Leibniz Information Center for EconomicsKiel UniversityKielGermany

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