Paired structures in knowledge representation

• Published in: Knowledge-based systems Vol. 100; pp. 50 - 58
• Main Authors: Montero, J., Bustince, H., Franco, C., Rodríguez, J.T., Gómez, D., Pagola, M., Fernández, J., Barrenechea, E.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.05.2016
• Subjects: Bipolarity; Formalism; Fuzzy logic; Fuzzy set theory; Knowledge base; Knowledge representation; Names; Neutral concepts; Paired structures; Semantics; Bipolarity; Paired structures; Knowledge representation; Neutral concepts
• ISSN: 0950-7051; 1872-7409
• DOI: 10.1016/j.knosys.2016.02.003

•An opposite-based approach to fuzzy modeling is presented as a unifying view to basic knowledge representation.•We claim that paired concepts are the basic building blocks for knowledge representation.•We claim the term paired fuzzy sets is not subject to potential conflict with other related theories.•We claim that neutrality arises from the semantic relationship between those paired concepts.•We show that valuation of paired concepts and neutrality through paired structures can naturally evolve into more complex valuation scales. In this position paper we propose a consistent and unifying view to all those basic knowledge representation models that are based on the existence of two somehow opposite fuzzy concepts. A number of these basic models can be found in fuzzy logic and multi-valued logic literature. Here it is claimed that it is the semantic relationship between two paired concepts what determines the emergence of different types of neutrality, namely indeterminacy, ambivalence and conflict, widely used under different frameworks (possibly under different names). It will be shown the potential relevance of paired structures, generated from two paired concepts together with their associated neutrality, all of them to be modeled as fuzzy sets. In this way, paired structures can be viewed as a standard basic model from which different models arise. This unifying view should therefore allow a deeper analysis of the relationships between several existing knowledge representation formalisms, providing a basis from which more expressive models can be later developed.