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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

ISBN: 978-989-758-049-9

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 ba sed 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. (More)

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Paper citation in several formats:
Nefzi, H.; Nefzi, H.; Farah, M.; Farah, M.; Riadh Farah, I.; Riadh Farah, I. and Solaiman, B. (2014). A Semi-automatic Mapping Selection in the Ontology Alignment Process.In Proceedings of the International Conference on Knowledge Engineering and Ontology Development - Volume 1: KEOD, (IC3K 2014) ISBN 978-989-758-049-9, pages 459-466. DOI: 10.5220/0005162204590466

@conference{keod14,
author={Hafed Nefzi. and Hafed Nefzi. and Mohamed Farah. and Mohamed Farah. and Imed Riadh Farah. and Imed Riadh Farah. and Basel Solaiman.},
title={A Semi-automatic Mapping Selection in the Ontology Alignment Process},
booktitle={Proceedings of the International Conference on Knowledge Engineering and Ontology Development - Volume 1: KEOD, (IC3K 2014)},
year={2014},
pages={459-466},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005162204590466},
isbn={978-989-758-049-9},
}

TY - CONF

JO - Proceedings of the International Conference on Knowledge Engineering and Ontology Development - Volume 1: KEOD, (IC3K 2014)
TI - A Semi-automatic Mapping Selection in the Ontology Alignment Process
SN - 978-989-758-049-9
AU - Nefzi, H.
AU - Nefzi, H.
AU - Farah, M.
AU - Farah, M.
AU - Riadh Farah, I.
AU - Riadh Farah, I.
AU - Solaiman, B.
PY - 2014
SP - 459
EP - 466
DO - 10.5220/0005162204590466

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