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

Version
1
Resource Type
Dataset
Creator
  • Schaible, Johann (GESIS)
  • Szekely, Pedro (USC Information Science Institute)
  • Scherp, Ansgar (Kiel University and ZBW)
Publication Date
2016
Classification
  • ZA:
    • Technology, Energy
Description

  • Abstract

    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.
Temporal Coverage
  • 2015-11-01 / 2016-01-31
  • 2015-11-01 / 2016-01-31
Geographic Coverage
  • Germany / DE
  • United States / US
Data and File Information
    • File Name: termpickerSurvey.pdf
      File Format: application/pdf
      File Size: 450167
      Data Fingerprint: a917a4fdf7d230dab405b8b1ab26ab33
      Method Fingerprint: MD5
    • File Name: termpickerSurvey.pdf
      File Format: application/pdf
      File Size: 450167
      Data Fingerprint: a917a4fdf7d230dab405b8b1ab26ab33
      Method Fingerprint: MD5
    • File Name: data_project_700359_2016_02_29.sav
      File Format: application/x-spss-sav
      File Size: 37398
      Data Fingerprint: fc736d4cfa32561372eb8e16e9e30f16
      Method Fingerprint: MD5
    • File Name: data_project_700359_2016_02_29.sav
      File Format: application/x-spss-sav
      File Size: 37398
      Data Fingerprint: fc736d4cfa32561372eb8e16e9e30f16
      Method Fingerprint: MD5
Availability
Download
Free Access (without Registration)
Publications
  • Comparing Vocabulary Term Recommendations using Association Rules and Learning To Rank: A User Study;

Update Metadata: 2019-10-01 | Issue Number: 3 | Registration Date: 2016-03-02