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.
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Notes
- 1.
In DL literature, an ontology is considered to be equivalent to a Knowledge Base.
- 2.
Wine dataset: http://archive.ics.uci.edu/ml/datasets/Wine.
- 3.
Landsat Science: http://landsat.gsfc.nasa.gov/.
- 4.
USGS Earth Explorer: http://earthexplorer.usgs.gov/.
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Acknowledgment
This work was supported by the French Agence Nationale de la Recherche under Grant ANR-12-MONU-0001.
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Chahdi, H., Grozavu, N., Mougenot, I., Bennani, Y., Berti-Equille, L. (2016). Towards Ontology Reasoning for Topological Cluster Labeling. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9949. Springer, Cham. https://doi.org/10.1007/978-3-319-46675-0_18
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