Quantifying Meaning: A Review of Semantic Information Analysis, Uncertainties, and Possibilistic Restrictions in Hybrid Systems

Jayesh Vijay Rao Karanjgaonkar

doi:https://doi.org/10.14445/22315373/IJMTT-V72I7P104

International Journal of Mathematics Trends and Technology

Research Article | Open Access | Download PDF

Volume 72 | Issue 7 | Year 2026 | Article Id. IJMTT-V72I7P104 | DOI : https://doi.org/10.14445/22315373/IJMTT-V72I7P104

Quantifying Meaning: A Review of Semantic Information Analysis, Uncertainties, and Possibilistic Restrictions in Hybrid Systems

Jayesh Vijay Rao Karanjgaonkar

Received	Revised	Accepted	Published
26 May 2026	01 Jul 2026	17 Jul 2026	30 Jul 2026

Citation :

Jayesh Vijay Rao Karanjgaonkar, "Quantifying Meaning: A Review of Semantic Information Analysis, Uncertainties, and Possibilistic Restrictions in Hybrid Systems," International Journal of Mathematics Trends and Technology (IJMTT), vol. 72, no. 7, pp. 25-35, 2026. Crossref, https://doi.org/10.14445/22315373/IJMTT-V72I7P104

Abstract

Classical information theory, originating with Shannon’s foundational work [1], treats information as a purely syntactic quantity divorced from meaning. Intelligent systems operating in real-world environments must nonetheless process semantic content carried in natural language — content permeated by vagueness, fuzziness, ambiguity, and multimodal uncertainty. This article presents a structured review of the conceptual and mathematical landscape surrounding semantic information theory, proceeding from Shannon’s technical framework through Weaver’s three-level communication model, Floridi’s strongly semantic theory, Zadeh’s restriction-centred view of natural language, and contemporary rate-distortion-based semantic communication theory, toward a possibilistic approach suited to modern human-machine hybrid systems. The review is organised along four axes: (i) a rigorous taxonomy of uncertainty types along the dimensions of nature, object, and severity; (ii) a hierarchy of Levels Of Abstraction (LOA) linking raw sensor data to high-level semantic content; (iii) a critical, comparative appraisal of classical and contemporary methodologies, including the Bar-Hillel–Carnap paradox, Floridi’s veridical resolution, and recent synonymous-mapping semantic communication theory; and (iv) the Natural Possibility (NP) Framework, a data-driven possibilistic account that converts unimodal frequency data into natural possibility distributions via a modal conversion rule, and defines workable measures — Proximity Value (PV), Elemental Proximity Value (EPV), Mean Proximity Value (MPV), and a semantic measure SM — for precisiating vague human commands in machine reasoning. The article identifies a specific gap in the existing literature — the absence of an operational bridge linking uncertainty taxonomy, the LOA hierarchy, and computable possibilistic measures — and evaluates the extent to which the possibilistic account addresses it, alongside its limitations relative to contemporary coding-theoretic semantic communication frameworks. Open research directions in approximate reasoning, explainable AI, and 6G semantic communication are identified.

Keywords

Hybrid Systems, Levels Of Abstraction, Possibilistic Restrictions, Possibility Theory, Semantic Information Theory.

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