Abstract Despite significant advances in mathematics, computation, and data processing, modern scientific and engineering systems remain constrained by ambiguity in data representation. This paper distinguishes between fundamental uncertainty inherent to physical systems and epistemic uncertainty introduced by inconsistent or ambiguous descriptions of objects and processes. CTMinfo is proposed as an ontological framework designed to eliminate epistemic uncertainty in non-organic domains through deterministic, verifiable descriptions. The paper hypothesizes that reducing ambiguity at the level of data representation can create improved conditions for scientific discovery and engineering efficiency, without contradicting established physical laws. Keywords: ontology, epistemic uncertainty, data accuracy, engineering data, semantic consistency 1. Introduction Contemporary science and industry rely heavily on probabilistic models and large-scale datasets (“Big Data”). While these approaches have enabled substantial progress, they also introduce systemic limitations related to data inconsistency, semantic ambiguity, and interpretational variance. This raises a fundamental question: To what extent are current limitations of scientific models caused not only by mathematics, but also by the quality and structure of underlying data? 2. Types of Uncertainty Scientific analysis typically involves two distinct types of uncertainty: 2.1 Fundamental (Ontological) Uncertainty Arises from inherent properties of physical reality, particularly at the quantum level. A canonical example is the Heisenberg Uncertainty Principle, which establishes limits on simultaneous measurement of certain physical quantities. 2.2 Epistemic Uncertainty Arises from incomplete, inconsistent, or ambiguous knowledge representations. This includes: - inconsistent classification systems - multiple naming conventions - loss of meaning across systems - human interpretation errors In practice, these two types of uncertainty are often conflated, leading to an overestimation of fundamental limits. 3. Problem: Ambiguity in Data Representation Modern engineering and economic systems frequently operate on data that lacks strict semantic consistency. Examples include: - identical objects described differently across systems - incompatible taxonomies and standards - ambiguity in specifications and procurement data Such inconsistencies introduce artificial uncertainty, reducing efficiency and obscuring underlying patterns. 4. CTMinfo Approach CTMinfo proposes a deterministic ontological framework for describing objects, components, and systems, particularly in non-organic domains. Core principles include: - unambiguous object description - strict ontological structure - verifiable attributes - elimination of semantic redundancy Within defined domains, this enables deterministic (effectively 100%) accuracy of description, in contrast to probabilistic approximations. 5. Hypothesis This paper proposes the following hypothesis: Future scientific progress may require not only new mathematical frameworks, but also a higher level of precision in data representation. If descriptions of objects and systems contain ambiguity, then any theoretical model built upon them inherits that ambiguity. By reducing epistemic uncertainty, CTMinfo may provide a cleaner substrate for: - detecting previously hidden patterns - resolving apparent contradictions - accelerating hypothesis testing 6. Implications Potential implications include: - increased efficiency in engineering and manufacturing systems - improved interoperability between data systems - reduction of errors in procurement and supply chains - enhanced conditions for scientific discovery Importantly, CTMinfo does not aim to replace existing scientific theories, but to improve the quality of data upon which they operate. 7. Limitations The approach has defined limitations: - currently applicable primarily to non-organic domains - does not eliminate fundamental physical uncertainty - does not replace theoretical physics or biological models Its role is infrastructural rather than theoretical. 8. Conclusion Scientific progress is traditionally associated with advances in theory and mathematics. However, the structure and quality of data may play an equally critical role. Before redefining the laws of nature, it may be necessary to redefine how reality is described. CTMinfo represents an attempt to address this foundational layer. References Heisenberg, W. (1927). On the quantum theoretical interpretation of kinematics and mechanics. Kuhn, T. (1962). The Structure of Scientific Revolutions. Wigner, E. (1960). The Unreasonable Effectiveness of Mathematics in the Natural Sciences.
Dmitriy V. Andriyanov (Mon,) studied this question.