Mustapha Bourahla

Work place: Computer Science Department, University of M’Sila, M’Sila, 28000, Algeria



Research Interests: Formal Methods, Artificial Intelligence


Mustapha Bourahla was born in Algeria and has Master degree from the University of Montreal (Canada) and PhD degree from the University of Biskra (Algeria) both in the field of computer science.

He has worked for Bell-Northern Research and Bell Canada for four years and now he is a professor in computer science department at the University of M’Sila (Algeria). He has many publications in the fields of formal methods, ontologies and artificial intelligence: LTL transformation modulo positive transitions. IET Comput. Digit. Tech. 11(6): 205-213 (2017), Description and reasoning for vague ontologies using logic programming. IET Softw. 12(5): 397-409 (2018) and Stability-based Dynamic Bayesian Network method for dynamic data mining. Eng. Appl. Artif. Intell. 77: 283-310 (2019).

Author Articles
Using Rough Set Theory for Reasoning on Vague Ontologies

By Mustapha Bourahla

DOI:, Pub. Date: 8 Aug. 2022

Web ontologies can contain vague concepts, which means the knowledge about them is imprecise and then query answering will not possible due to the open world assumption. A concept description can be very exact (crisp concept) or exact (fuzzy concept) if its knowledge is complete, otherwise it is inexact (vague concept) if its knowledge is incomplete. In this paper, we propose a method based on the rough set theory for reasoning on vague ontologies. With this method, the detection of vague concepts will insert into the original ontology new rough vague concepts where their description is defined on approximation spaces to be used by extended Tableau algorithm for automatic reasoning. A prototype of Tableau's extended algorithm is developed and tested on examples where encouraging results are given by this method to demonstrate that unlike other methods, it is possible to answer queries even in the presence of incomplete information.

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