BetterRelations: collecting association strengths for linked data triples with a game

  • Authors:

  • Jörn Hees;Thomas Roth-Berghofer;Ralf Biedert;Benjamin Adrian;Andreas Dengel

  • Affiliations:

  • Computer Science Department, University of Kaiserslautern, Germany,Knowledge Management Department, DFKI GmbH, Kaiserslautern, Germany;Knowledge Management Department, DFKI GmbH, Kaiserslautern, Germany,School of Computing and Technology, University of West London, UK;Knowledge Management Department, DFKI GmbH, Kaiserslautern, Germany;Knowledge Management Department, DFKI GmbH, Kaiserslautern, Germany;Computer Science Department, University of Kaiserslautern, Germany,Knowledge Management Department, DFKI GmbH, Kaiserslautern, Germany

  • Venue:
  • Search Computing
  • Year:
  • 2012

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Abstract

The simulation of human thinking is one of the long term goals of the Artificial Intelligence community. In recent years, the adoption of Semantic Web technologies and the ongoing sharing of Linked Data has generated one of the world's largest knowledge bases, bringing us closer to this dream than ever. Nevertheless, while associations in the human memory have different strengths, such explicit association strengths (edge weights) are missing in Linked Data. Hence, finding good heuristics which can estimate human-like association strengths for Linked Data facts (triples) is of major interest to us. In order to evaluate existing approaches with respect to human-like association strengths, we need a collection of such explicit edge weights for Linked Data triples. In this chapter we first provide an overview of existing approaches to rate Linked Data triples which could be valuable candidates for good heuristics. We then present a web-game prototype which can help with the collection of a ground truth of edge weights for triples. We explain the game's concept, summarize Linked Data related implementation aspects, and include a detailed evaluation of the game.