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

Conference paper

DOI: 10.1007/978-3-319-34129-3_14

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9678)
Cite this paper as:
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., Ponzetto S., Lange C. (eds) The Semantic Web. Latest Advances and New Domains. ESWC 2016. Lecture Notes in Computer Science, vol 9678. Springer, Cham

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.

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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