Formalization of Ontology Conceptualizations Using Model Transformation

Author:

Boudia Malika1,Bourahla Mustapha1

Affiliation:

1. University of M'Sila, Algeria

Abstract

Conceptual models are built with concepts and relationships between them to reach a unified view of domain problems. There are many kinds of conceptual models developed in different modeling languages, such as class diagrams and entity-relationship models. In this paper, the authors have developed a specific meta-model following the Ecore standard to define conceptual models. These domain-specific conceptual models can be automatically formalized as domain ontologies using model transformation with the technique of triple graph grammars into ontology formal descriptions in accordance with the defined Ecore meta-model of the language OWL (web ontology language). For ontology deployment, its OWL code may be generated from OWL models using model-to-code transformation guided by Xpand templates. A performance evaluation is realized using a benchmark from the university domain with very large conceptual models. Through the experiments, they validate the performance and we prove the exactness and the scalability of the automatic transformation process of conceptual models.

Publisher

IGI Global

Subject

Management of Technology and Innovation,Information Systems

Reference35 articles.

1. Belghiat, A., & Bourahla, M. (2012). Automatic Generation of OWL Ontologies from UML Class Diagrams Based on MetaModelling and Graph Grammars. International Science Index, Computer and Information Engineering, 6(8).

2. Bernaschina, C. (2017). ALMOsT.js: An Agile Model to Model and Model to Text Transformation Framework. ICWE 2017: Web Engineering, 79-97.

3. Boris, M., Peter, F., Patel-Schneider, P.F., & Parsia, B. (Eds.). (2012). OWL 2 Web Ontology Language Specification: Structural Specification and Functional-Style Syntax. W3C Recommendation. Available at: https://www.w3.org/TR/owl2-syntax/https://www.w3.org/2012/pdf/REC-owl2-syntax-20121211.pdf

4. Description and reasoning for vague ontologies using logic programming

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