Towards ontology reasoning for topological cluster labeling

Hatim Chahdi, Nistor Grozavu, Isabelle Mougenot, Younès Bennani, Laure Berti-Equille

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationNeural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings
PublisherSpringer Verlag
Pages156-164
Number of pages9
Volume9949 LNCS
ISBN (Print)9783319466743
DOIs
Publication statusPublished - 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9949 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Chahdi, H., Grozavu, N., Mougenot, I., Bennani, Y., & Berti-Equille, L. (2016). Towards ontology reasoning for topological cluster labeling. In Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings (Vol. 9949 LNCS, pp. 156-164). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9949 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46675-0_18