Bridging Data and Meaning: A Review of Semantic Information Theory with Fuzzy Linguistic and Possibilistic Techniques

Semantic Information Theory (SIT) extends classical information theory by focusing on the meaningful content of communicated data rather than mere symbol transmission. This review paper bridges the foundational theories of semantic information—such as those by Bar-Hillel, Carnap, MacKay, and Floridi—with modern advances utilising fuzzy linguistic and possibilistic techniques. Classical approaches provide quantitative and logical frameworks but often struggle to handle vagueness, context, and meaning in natural language and human communication. Fuzzy set theory and possibility distributions, pioneered by Zadeh and further developed by contemporary researchers, offer robust mathematical tools to model semantic content under uncertainty and imprecision. These modern methods encode meaning as restrictions and graded membership functions, enabling more nuanced measurement and interpretation of semantic information that better aligns with human linguistic and cognitive processes. Integrating these perspectives, the paper highlights the theoretical evolution from syntactic to semantic measures, discusses practical benefits in AI and hybrid systems, and underscores ongoing challenges in formalising a comprehensive, operational semantic information framework. This synthesis provides a cohesive understanding of how data and meaning converge in semantic information theory, fostering progress in knowledge representation, decision making, and intelligent communication systems.