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Quality Prediction in Collaborative Platforms: A Generic Approach by Heterogeneous Graphs

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Database and Expert Systems Applications (DEXA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9828))

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Abstract

As everyone can enrich or rather impoverish crowd-sourcing contents, it is a crucial need to continuously improve automatic quality contents assessment tools. Structural-based analysis methods developed for such quality prediction purposes generally handle a limited or manually fixed number of families of nodes and relations. This lack of genericity prevents existing algorithms for being adaptable to platforms evolutions. In this work, we propose a generic and adaptable algorithm, called HSQ, generalising various state-of-the-art models and allowing the consideration of graphs defined by an arbitrary number of nodes semantics. Evaluations performed over the two representative crowd-sourcing platforms Wikipedia and Stack Exchange state that the consideration of additional nodes semantics and relations improve the performances of state-of-the-art approaches.

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Notes

  1. 1.

    https://en.wikipedia.org/wiki.

  2. 2.

    http://blog.stackoverflow.com/2009/06/stack-overflow-creative-commons-data-dump/.

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Authors and Affiliations

  1. Institut de Recherche en Informatique de Toulouse, 118 Route de Narbonne, 31071, Toulouse, France

    Baptiste de La Robertie, Yoann Pitarch & Olivier Teste

Authors
  1. Baptiste de La Robertie
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  2. Yoann Pitarch
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  3. Olivier Teste
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Correspondence to Baptiste de La Robertie .

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Editors and Affiliations

  1. Clausthal University of Technology, Clausthal-Zellerfeld, Germany

    Sven Hartmann

  2. Victoria University of Wellington, Wellington, New Zealand

    Hui Ma

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de La Robertie, B., Pitarch, Y., Teste, O. (2016). Quality Prediction in Collaborative Platforms: A Generic Approach by Heterogeneous Graphs. In: Hartmann, S., Ma, H. (eds) Database and Expert Systems Applications. DEXA 2016. Lecture Notes in Computer Science(), vol 9828. Springer, Cham. https://doi.org/10.1007/978-3-319-44406-2_3

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  • DOI: https://doi.org/10.1007/978-3-319-44406-2_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44405-5

  • Online ISBN: 978-3-319-44406-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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