One of the major challenges in modeling a real-world domain is how to effectively represent uncertain and incomplete knowledge of that domain. Several techniques for representing uncertainty in ontologies have been proposed with some of the techniques lacking provision for vague inference. The classical tableaux-based algorithm does not provide the flexibility for reasoning over such vague ontologies. However, several extensions of the tableaux-based algorithm have been proposed to cope with fuzzy reasoning. Similarly, several alternative reasoning methods for incomplete, inconsistent, and uncertain ontologies have been proposed. One of the major limitations of most of those techniques is that they require reengineering existing ontologies to cope with uncertainty. This paper proposes a satisfiability algorithm for vague ontologies that uses a rough set to approximate the concepts and roles. The proposed technique takes advantage of the existing ontology knowledge base to achieve vague reasoning without the need of reengineering the ontology. The results show that the proposed technique conforms to the tableaux-based algorithm while providing a way of reasoning over the uncertain aspects of ontologies.
Donfack Kana, Armand Florentin and Akinkunmi, Babatunde Opeoluwa
"Fuzzy Reasoning Procedure for Ontologies based on Rough Membership Approximation,"
Future Computing and Informatics Journal: Vol. 7:
1, Article 5.
Available at: https://digitalcommons.aaru.edu.jo/fcij/vol7/iss1/5