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Extended Semantic Web Conference

ESWC 2012: The Semantic Web: Research and Applications pp 24–38Cite as

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Linked Data-Based Concept Recommendation: Comparison of Different Methods in Open Innovation Scenario

Linked Data-Based Concept Recommendation: Comparison of Different Methods in Open Innovation Scenario

  • Danica Damljanovic21,
  • Milan Stankovic22,23 &
  • Philippe Laublet23 
  • Conference paper
  • 3142 Accesses

  • 22 Citations

Part of the Lecture Notes in Computer Science book series (LNISA,volume 7295)

Abstract

Concept recommendation is a widely used technique aimed to assist users to chose the right tags, improve their Web search experience and a multitude of other tasks. In finding potential problem solvers in Open Innovation (OI) scenarios, the concept recommendation is of a crucial importance as it can help to discover the right topics, directly or laterally related to an innovation problem. Such topics then could be used to identify relevant experts. We propose two Linked Data-based concept recommendation methods for topic discovery. The first one, hyProximity, exploits only the particularities of Linked Data structures, while the other one applies a well-known Information Retrieval method, Random Indexing, to the linked data. We compare the two methods against the baseline in the gold standard-based and user study-based evaluations, using the real problems and solutions from an OI company.

Keywords

  • concept recommendation
  • structure-based similarity
  • semantic similarity
  • information retrieval
  • statistical semantics
  • linked data
  • ontologies
  • recommender systems
  • concept discovery
  • open innovation

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Sheffield, United Kingdom

    Danica Damljanovic

  2. Hypios, 187 rue du Temple, 75003, Paris, France

    Milan Stankovic

  3. STIH, Université Paris-Sorbonne, 28 rue Serpente, 75006, Paris, France

    Milan Stankovic & Philippe Laublet

Authors
  1. Danica Damljanovic
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  2. Milan Stankovic
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  3. Philippe Laublet
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Editor information

Editors and Affiliations

  1. Institute AIFB, Karlsruhe Institute of Technology, Englerstrasse 11, 76131, Karlsruhe, Germany

    Elena Simperl

  2. CITEC, University of Bielefeld, Morgenbreede 39, 33615, Bielefeld, Germany

    Philipp Cimiano

  3. Siemens AG Österreich, Siemensstrasse 90, 1210, Vienna, Austria

    Axel Polleres

  4. Technical University of Madrid, C/ Severo Ochoa, 13, 28660, Boadilla del Monte, Madrid, Spain

    Oscar Corcho

  5. STLab, ISTC-CNR, Via Nomentana 56, 00161, Rome, Italy

    Valentina Presutti

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© 2012 Springer-Verlag Berlin Heidelberg

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Cite this paper

Damljanovic, D., Stankovic, M., Laublet, P. (2012). Linked Data-Based Concept Recommendation: Comparison of Different Methods in Open Innovation Scenario. In: Simperl, E., Cimiano, P., Polleres, A., Corcho, O., Presutti, V. (eds) The Semantic Web: Research and Applications. ESWC 2012. Lecture Notes in Computer Science, vol 7295. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30284-8_9

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  • DOI: https://doi.org/10.1007/978-3-642-30284-8_9

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  • Print ISBN: 978-3-642-30283-1

  • Online ISBN: 978-3-642-30284-8

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