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Semantic Models for Adaptive Interactive SystemsVisualizing Search Results of Linked Open Data

Semantic Models for Adaptive Interactive Systems: Visualizing Search Results of Linked Open Data [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.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Semantic Models for Adaptive Interactive SystemsVisualizing Search Results of Linked Open Data

Part of the Human–Computer Interaction Series Book Series
Editors: Hussein, Tim; Paulheim, Heiko; Lukosch, Stephan; Ziegler, Jürgen; Calvary, Gaëlle
Stab, Christian; Burkhardt, Dirk; Breyer, Matthias; Nazemi, Kawa
Semantic Models for Adaptive Interactive Systems — May 14, 2013
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Publisher
Springer London
Copyright
© Springer-Verlag London 2013
ISBN
978-1-4471-5300-9
Pages
133 –149
DOI
10.1007/978-1-4471-5301-6_7
Publisher site
See Chapter on Publisher Site

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.]

Published: May 14, 2013

Keywords: Search Result; Semantic Information; Query Term; Task Completion Time; Result List

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