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Semantic Models for Adaptive Interactive Systems pp 133–149Cite as

Visualizing Search Results of Linked Open Data

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Part of the book series: Human–Computer Interaction Series ((HCIS))

Abstract

Finding accurate information of high quality is still a challenging task particularly with regards to the increasing amount of resources in current information systems. This is especially true if policy decisions that impact humans, economy or environment are based on the demanded information. For improving search result generation and analyzing user queries more and more information retrieval systems utilize Linked Open Data and other semantic knowledge bases. Nevertheless, the semantic information that is used during search result generation mostly remains hidden from the users although it significantly supports users in understanding and assessing search results. The presented approach combines information visualizations with semantic information for offering visual feedback about the reasons the results were retrieved. It visually represents the semantic interpretation and the relation between query terms and search results to offer more transparency in search result generation and allows users to unambiguously assess the relevance of the retrieved resources for their individual search. The approach also supports the common search strategies by providing visual feedback for query refinement and enhancement. Besides the detailed description of the search system, an evaluation of the approach shows that the use of semantic information considerably supports users in assessment and decision-making tasks.

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Notes

  1. 1.

    http://academic.research.microsoft.com.

  2. 2.

    For the prototype we use the DBpedia lookup service that is available at http://wiki.dbpedia.org/lookup/.

  3. 3.

    SemaVis Framework: http://www.semavis.net.

  4. 4.

    A version using the DBpedia database and the described search processing is available at http://semanticsearch.semavis.net.

  5. 5.

    Measured on a five point scale (5 = very much experience; 1 = very little experience) in the demographic part of the questionnaire.

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Authors and Affiliations

  1. Fraunhofer Institute for Computer Graphics Research (IGD), Darmstadt, Germany

    Christian Stab, Dirk Burkhardt, Matthias Breyer & Kawa Nazemi

Authors
  1. Christian Stab
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  2. Dirk Burkhardt
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  3. Matthias Breyer
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  4. Kawa Nazemi
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Corresponding author

Correspondence to Christian Stab .

Editor information

Editors and Affiliations

  1. Interactive Systems Group, University of Duisburg-Essen, Forsthausweg 2, Duisburg, 47057, Germany

    Tim Hussein

  2. Data and Web Science Group, University of Mannheim, Bissmarkstr., Mannheim, 68159, Germany

    Heiko Paulheim

  3. Faculty of Technology, Policy & Manageme, Delft University of Technology, Jaffalaan 5, Delft, 2628BX, Zuid-Holland, Netherlands

    Stephan Lukosch

  4. Interactive Systems Group, University of Duisburg-Essen, Forsthausweg 2, Duisburg, 47057, Germany

    Jürgen Ziegler

  5. Grenoble Informatics Laboratory, Grenoble Institute of Technology, Rue des Mathématiques 41, Grenoble, 38041, France

    Gaëlle Calvary

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Stab, C., Burkhardt, D., Breyer, M., Nazemi, K. (2013). Visualizing Search Results of Linked Open Data. In: Hussein, T., Paulheim, H., Lukosch, S., Ziegler, J., Calvary, G. (eds) Semantic Models for Adaptive Interactive Systems. Human–Computer Interaction Series. Springer, London. https://doi.org/10.1007/978-1-4471-5301-6_7

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  • DOI: https://doi.org/10.1007/978-1-4471-5301-6_7

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-5300-9

  • Online ISBN: 978-1-4471-5301-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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