Structuring Abstraction to Achieve Ontology Modularisation

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Author(s): Zubeida Khan (Council for Scientific and Industrial Research, Pretoria, South Africa)and C. Maria Keet (Department of Computer Science, University of Cape Town, South Africa)
Copyright: 2021
Pages: 21
Source title: Advanced Concepts, Methods, and Applications in Semantic Computing
Source Author(s)/Editor(s): Olawande Daramola (Cape Peninsula University of Technology, South Africa)and Thomas Moser (St. Pölten University of Applied Sciences, Austria)
DOI: 10.4018/978-1-7998-6697-8.ch004

Keywords: Computational Linguistics / Computer Science & IT / Data Analysis and Statistics / Information Science Reference

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Abstract

Large and complex ontologies lead to usage difficulties, thereby hampering the ontology developers' tasks. Ontology modules have been proposed as a possible solution, which is supported by some algorithms and tools. However, the majority of types of modules, including those based on abstraction, still rely on manual methods for modularisation. Toward filling this gap in modularisation techniques, the authors systematised abstractions and selected five types of abstractions relevant for modularisation for which they created novel algorithms, implemented them, and wrapped them in a GUI, called NOMSA, to facilitate their use by ontology developers. The algorithms were evaluated quantitatively by assessing the quality of the generated modules. The quality of a module is measured by comparing it to the benchmark metrics from an existing framework for ontology modularisation. The results show that the module's quality ranges between average to good, whilst also eliminating manual intervention.

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