Expert Opinion Extraction from a Biomedical Database

  • Ahmed Samet
  • Thomas Guyet
  • Benjamin Negrevergne
  • Tien-Tuan Dao
  • Tuan Nha Hoang
  • Marie Christine Ho Ba Tho
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10369)


In this paper, we tackle the problem of extracting frequent opinions from uncertain databases. We introduce the foundation of an opinion mining approach with the definition of pattern and support measure. The support measure is derived from the commitment definition. A new algorithm called OpMiner that extracts the set of frequent opinions modelled as a mass functions is detailed. Finally, we apply our approach on a real-world biomedical database that stores opinions of experts to evaluate the reliability level of biomedical data. Performance analysis showed a better quality patterns for our proposed model in comparison with literature-based methods.


Uncertain database Data mining Opinion OpMiner 



This work is a part of the PEPS project funded by the French national agency for medicines and health products safety (ANSM), and of the SePaDec project funded by Region Bretagne.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Ahmed Samet
    • 1
  • Thomas Guyet
    • 1
  • Benjamin Negrevergne
    • 3
  • Tien-Tuan Dao
    • 2
  • Tuan Nha Hoang
    • 2
  • Marie Christine Ho Ba Tho
    • 2
  1. 1.Université Rennes 1/IRISA-UMR6074RennesFrance
  2. 2.Sorbonne University, Université de technologie de Compiègne CNRS, UMR 7338 Biomechanics and BioengineeringCompiègneFrance
  3. 3.LAMSADE, Université Paris-DauphineParisFrance

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