Combining human and computation intelligence: the case of data interlinking tools

  • Authors:

  • Elena Simperl;Stephan Wölger;Stefan Thaler;Barry Norton;Tobias Bürger

  • Affiliations:

  • Karlsruhe Institute of Technology, Germany;STI Innsbruck, University of Innsbruck, Austria;STI Innsbruck, University of Innsbruck, Austria;Ontotext AD, UK;Payback GmbH, Germany

  • Venue:
  • International Journal of Metadata, Semantics and Ontologies
  • Year:
  • 2012

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Abstract

Interlinking is without doubt one of the most active and mature areas of research and development in semantic technologies. Over the last decade or more a multitude of approaches to match, merge and integrate ontologies, both at the schema and instance levels have been proposed and successfully applied to resolve heterogeneity issues and, more recently, to interlink RDF data sets exposed over the Web as part of the Linked Open Data Cloud. The strengths and weaknesses of existing interlinking solutions, as well as their natural limitations and principled combinations have been intensively studied, not least through community projects such as the Ontology Alignment Evaluation Initiative. Human input remains a key ingredient of the process, either as a source of domain knowledge used to train matching algorithms and to build the underlying knowledge base, or to validate automatically computed results. In this paper we describe how such human input could be acquired and used to enhance the results of existing data interlinking technology via crowdsourcing. In a survey of data interlinking tools we identify several aspects of the interlinking process that crucially rely on human contributions and explain how these aspects could be subject to a semantically enabled human computation architecture that can be set-up by extending interlinking platforms such as Silk with direct interfaces to popular microtask platforms such as Amazon's Mechanical Turk.