Towards Ontology Reasoning for Topological Cluster Labeling

  • Hatim ChahdiEmail author
  • Nistor Grozavu
  • Isabelle Mougenot
  • Younès Bennani
  • Laure Berti-Equille
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9949)


In this paper, we present a new approach combining topological unsupervised learning with ontology based reasoning to achieve both: (i) automatic interpretation of clustering, and (ii) scaling ontology reasoning over large datasets. The interest of such approach holds on the use of expert knowledge to automate cluster labeling and gives them high level semantics that meets the user interest. The proposed approach is based on two steps. The first step performs a topographic unsupervised learning based on the SOM (Self-Organizing Maps) algorithm. The second step integrates expert knowledge in the map using ontology reasoning over the prototypes and provides an automatic interpretation of the clusters. We apply our approach to the real problem of satellite image classification. The experiments highlight the capacity of our approach to obtain a semantically labeled topographic map and the obtained results show very promising performances.


Normalize Difference Vegetation Index Description Logic Unsupervised Learning Cluster Label Normalize Difference Water Index 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported by the French Agence Nationale de la Recherche under Grant ANR-12-MONU-0001.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Hatim Chahdi
    • 1
    • 2
    Email author
  • Nistor Grozavu
    • 2
  • Isabelle Mougenot
    • 1
  • Younès Bennani
    • 2
  • Laure Berti-Equille
    • 1
    • 3
  1. 1.Espace-Dev UMR 228, IRD - Université de MontpellierMontpellierFrance
  2. 2.LIPN CNRS UMR 7030, CNRS - Université Paris 13VilletaneuseFrance
  3. 3.Qatar Computing Research InstituteHamad Bin Khalifa UniversityDohaQatar

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