A similarity-based framework for the alignment of an ontology for remote sensing. (November 2016)

• Record Type: Journal Article
• Authors: Farah, Mohamed; Nefzi, Hafedh; Farah, Imed Riadh
• Is Part Of: Computers & geosciences. Volume 96(2016)
• Page Start: 202
• Page End: 207
• Publication Date: 2016-11
• Subjects: Remote sensing -- Ontology conceptualization -- Ontology alignment -- Similarity measures -- Dimensionality reduction -- Ordinal regression model; Environmental policy -- Periodicals; 550.5
• DOI: 10.1016/j.cageo.2016.08.018
• Languages: English
• ISSNs: 0098-3004

Abstract

Abstract: Building remote sensing (RS) ontologies can undoubtedly help automatic interpretation of RS images content. Ontology alignment is proven to be an effective ontology building process that enables reusing already existing semantic resources. The quality of the ontology alignment output highly depends on the similarity measures that have been considered as well as the way they are combined together. In the literature, research on similarity measures mainly focuses on how to build new or refine already existing similarity measures leading to a wide range of measures. However, few research addresses their dependencies and combination in order to evaluate the overall similarity of the concepts to be compared. In this paper, we first show how to select a reduced set of similarity measures to be used in the alignment process. Afterwards, we present a ranking model that allows sorting mappings between concepts coming from two different ontologies in a decreasing order of global similarity score. First experimentation shows that the proposed approach is promising. Abstract : Highlights: Ontologies can help automatic interpretation of remote sensing images content. Ontology alignment effectively helps building RS ontologies using semantic resources. The alignment process is carried out using a reduced set of similarity measures. A ranking model allows sorting mappings in a decreasing order of global similarity.