Towards Ontology Reasoning for Topological Cluster Labeling

Abstract : In this paper, we present a new approach combining topological un-supervised 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 ontol-ogy 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.
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Communication dans un congrès
International Conference on Neural Information Processing, Oct 2016, Kyoto, Japan. Lecture Notes in Computer Science, Springer, 9949, pp.156 - 164, 2016, Lecture Notes in Computer Science, Springer. 〈http://link.springer.com/book/10.1007/978-3-319-46675-0〉. 〈10.1007/978-3-319-46675-0_18〉
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Hatim Chahdi, Nistor Grozavu, Isabelle Mougenot, Younès Bennani, Laure Berti-Equille. Towards Ontology Reasoning for Topological Cluster Labeling. International Conference on Neural Information Processing, Oct 2016, Kyoto, Japan. Lecture Notes in Computer Science, Springer, 9949, pp.156 - 164, 2016, Lecture Notes in Computer Science, Springer. 〈http://link.springer.com/book/10.1007/978-3-319-46675-0〉. 〈10.1007/978-3-319-46675-0_18〉. 〈hal-01438892〉

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