Authors:
Hafed Nefzi
1
;
Mohamed Farah
1
;
Imed Riadh Farah
2
and
Basel Solaiman
3
Affiliations:
1
University of Manouba, Tunisia
;
2
University of Manouba and TELECOM-Bretagne, Tunisia
;
3
TELECOM-Bretagne, France
Keyword(s):
Ontology, Remote Sensing, Similarity Models and Measures, Alignment, Enrichment, UTA.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Concept Mining
;
Data Engineering
;
Enterprise Information Systems
;
Evolutionary Computing
;
Information Extraction
;
Information Systems Analysis and Specification
;
Knowledge Discovery and Information Retrieval
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Machine Learning
;
Ontologies and the Semantic Web
;
Ontology Engineering
;
Ontology Matching and Alignment
;
Ontology Sharing and Reuse
;
Soft Computing
;
Symbolic Systems
Abstract:
Ontologies are considered as one of the most powerful tools for knowledge representation and reasoning. Thus, they are considered as a fundamental support for image annotation, indexing and retrieval. In order to build a remote sensing satellite image ontology that models the geographic objects that we find in a scene, their characteristics as well as their relationships, we propose to reuse existing geographic ontologies to enrich an ontological core. Reusing high quality resources (called source ontologies) helps ensuring a good quality for the extracted knowledge, and alleviating the conceptualization stage, i.e. avoiding building a new ontology from scratch. Ontology alignment is an important phase within the enrichment process. It is a process that allows discovering mappings between core and source ontologies, where each mapping is a couple of entities brought from each ontology and linked together either by an equivalence or a subsumption relationship. Such relationships are b
ased on various similarity measures. In this paper, we first present a brief literature review of existing theoretical frameworks for similarity measures, then we describe a new alignment approach based on a semi-automatic mapping selection process that needs little human intervention. First experiments show the benefit from using the proposed approach.
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