ARS/SD: An Associative Retrieval Service for the Semantic Desktop

  • Peter Scheir
  • Chiara Ghidini
  • Roman Kern
  • Michael Granitzer
  • Stefanie N. Lindstaedt
Part of the Studies in Computational Intelligence book series (SCI, volume 221)


While it is agreed that semantic enrichment of resources would lead to better search results, at present the low coverage of resources on the web with semantic information presents a major hurdle in realizing the vision of search on the Semantic Web. To address this problem we investigate how to improve retrieval performance in a setting where resources are sparsely annotated with semantic information. We suggest employing techniques from associative information retrieval to find relevant material, which was not originally annotated with the concepts used in a query. We present an associative retrieval service for the Semantic Desktop and evaluate if the use of associative retrieval techniques increases retrieval performance.

Evaluation of new retrieval paradigms, as retrieval in the Semantic Web or on the Semantic Desktop, presents an additional challenge as no off-the-shelf test corpora for evaluation exist. Hence we give a detailed description of the approach taken to the evaluation of the information retrieval service we have built for the Semantic Desktop.


Information Retrieval Semantic Similarity Domain Ontology Query Expansion Semantic Annotation 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Peter Scheir
    • 1
  • Chiara Ghidini
    • 2
  • Roman Kern
    • 3
  • Michael Granitzer
    • 3
  • Stefanie N. Lindstaedt
    • 3
  1. 1.Graz University of TechnologyAustria
  2. 2.Fondazione Bruno KesslerItaly
  3. 3.Know-CenterGrazAustria

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