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Conference papers
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.
Cited literature [20 references]
https://hal.archives-ouvertes.fr/hal-01438892
Contributor : Hatim Chahdi <>
Submitted on : Wednesday, January 18, 2017 - 11:08:16 AM
Last modification on : Tuesday, December 8, 2020 - 10:34:04 AM
Long-term archiving on: : Wednesday, April 19, 2017 - 1:34:13 PM
Contributor : Hatim Chahdi <>
Submitted on : Wednesday, January 18, 2017 - 11:08:16 AM
Last modification on : Tuesday, December 8, 2020 - 10:34:04 AM
Long-term archiving on: : Wednesday, April 19, 2017 - 1:34:13 PM
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