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Extended Semantic Web Conference

ESWC 2012: The Semantic Web: Research and Applications pp 39–55Cite as

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  2. The Semantic Web: Research and Applications
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Finding Co-solvers on Twitter, with a Little Help from Linked Data

Finding Co-solvers on Twitter, with a Little Help from Linked Data

  • Milan Stankovic21,23,
  • Matthew Rowe22 &
  • Philippe Laublet23 
  • Conference paper
  • 2822 Accesses

  • 10 Citations

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

Abstract

In this paper we propose a method for suggesting potential collaborators for solving innovation challenges online, based on their competence, similarity of interests and social proximity with the user. We rely on Linked Data to derive a measure of semantic relatedness that we use to enrich both user profiles and innovation problems with additional relevant topics, thereby improving the performance of co-solver recommendation. We evaluate this approach against state of the art methods for query enrichment based on the distribution of topics in user profiles, and demonstrate its usefulness in recommending collaborators that are both complementary in competence and compatible with the user. Our experiments are grounded using data from the social networking service Twitter.com.

Keywords

  • Linked Data
  • Twitter
  • Collaborator Recommendation

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Author information

Authors and Affiliations

  1. Hypios Research, 187 rue du Temple, 75003, Paris, France

    Milan Stankovic

  2. Knowledge Media Institute, The Open University, Milton Keynes, UK, MK7 6AA

    Matthew Rowe

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

    Milan Stankovic & Philippe Laublet

Authors
  1. Milan Stankovic
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  2. Matthew Rowe
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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

Stankovic, M., Rowe, M., Laublet, P. (2012). Finding Co-solvers on Twitter, with a Little Help from Linked Data. 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_10

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  • DOI: https://doi.org/10.1007/978-3-642-30284-8_10

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  • Print ISBN: 978-3-642-30283-1

  • Online ISBN: 978-3-642-30284-8

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