Skip to Main content Skip to Navigation
Conference papers

On the Use of Ontology as a priori Knowledge into Constrained Clustering

Abstract : Recent studies have shown that the use of a priori knowledge can significantly improve the results of unsupervised classification. However, capturing and formatting such knowledge as constraints is not only very expensive requiring the sustained involvement of an expert but it is also very difficult because some valuable information can be lost when it cannot be encoded as constraints. In this paper, we propose a new constraint-based clustering approach based on ontology reasoning for automatically generating constraints and bridging the semantic gap in satellite image labeling. The use of ontology as a priori knowledge has many advantages that we leverage in the context of satellite image interpretation. The experiments we conduct have shown that our proposed approach can deal with incomplete knowledge while completely exploiting the available one.
Complete list of metadatas

Cited literature [27 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01400122
Contributor : Hatim Chahdi <>
Submitted on : Monday, November 21, 2016 - 2:24:18 PM
Last modification on : Saturday, February 15, 2020 - 2:04:39 AM
Document(s) archivé(s) le : Tuesday, March 21, 2017 - 5:06:50 AM

File

PREPRINT.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01400122, version 1

Citation

Hatim Chahdi, Nistor Grozavu, Isabelle Mougenot, Laure Berti-Equille, Younès Bennani. On the Use of Ontology as a priori Knowledge into Constrained Clustering. IEEE International Conference on Data Science and Advanced Analytics (DSAA), Oct 2016, Montreal, Canada. ⟨hal-01400122⟩

Share

Metrics

Record views

912

Files downloads

436