Gardado en:
| Autor Principal: | |
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| Formato: | Recurso digital |
| Idioma: | inglés |
| Publicado: |
Zenodo
2026
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| Subjects: | |
| Acceso en liña: | https://doi.org/10.5281/zenodo.19326817 |
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Table of Contents:
- <div dir="auto">Abstract</div> <div dir="auto">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.</div> <div dir="auto"> </div> <div dir="auto">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.</div> <div dir="auto"> </div> <div dir="auto">Keywords: ontology, epistemic uncertainty, data accuracy, engineering data, semantic consistency</div> <div dir="auto"> </div> <div dir="auto">1. Introduction</div> <div dir="auto">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.</div> <div dir="auto"> </div> <div dir="auto">This raises a fundamental question:</div> <div dir="auto"> </div> <div dir="auto">To what extent are current limitations of scientific models caused not only by mathematics, but also by the quality and structure of underlying data?</div> <div dir="auto"> </div> <div dir="auto">2. Types of Uncertainty</div> <div dir="auto">Scientific analysis typically involves two distinct types of uncertainty:</div> <div dir="auto"> </div> <div dir="auto">2.1 Fundamental (Ontological) Uncertainty</div> <div dir="auto">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.</div> <div dir="auto"> </div> <div dir="auto">2.2 Epistemic Uncertainty</div> <div dir="auto">Arises from incomplete, inconsistent, or ambiguous knowledge representations. This includes:</div> <div dir="auto"> </div> <div dir="auto">- inconsistent classification systems </div> <div dir="auto">- multiple naming conventions </div> <div dir="auto">- loss of meaning across systems </div> <div dir="auto">- human interpretation errors </div> <div dir="auto"> </div> <div dir="auto">In practice, these two types of uncertainty are often conflated, leading to an overestimation of fundamental limits.</div> <div dir="auto"> </div> <div dir="auto">3. Problem: Ambiguity in Data Representation</div> <div dir="auto">Modern engineering and economic systems frequently operate on data that lacks strict semantic consistency.</div> <div dir="auto"> </div> <div dir="auto">Examples include:</div> <div dir="auto"> </div> <div dir="auto">- identical objects described differently across systems </div> <div dir="auto">- incompatible taxonomies and standards </div> <div dir="auto">- ambiguity in specifications and procurement data </div> <div dir="auto"> </div> <div dir="auto">Such inconsistencies introduce artificial uncertainty, reducing efficiency and obscuring underlying patterns.</div> <div dir="auto"> </div> <div dir="auto">4. CTMinfo Approach</div> <div dir="auto">CTMinfo proposes a deterministic ontological framework for describing objects, components, and systems, particularly in non-organic domains.</div> <div dir="auto"> </div> <div dir="auto">Core principles include:</div> <div dir="auto"> </div> <div dir="auto">- unambiguous object description </div> <div dir="auto">- strict ontological structure </div> <div dir="auto">- verifiable attributes </div> <div dir="auto">- elimination of semantic redundancy </div> <div dir="auto"> </div> <div dir="auto">Within defined domains, this enables deterministic (effectively 100%) accuracy of description, in contrast to probabilistic approximations.</div> <div dir="auto"> </div> <div dir="auto">5. Hypothesis</div> <div dir="auto">This paper proposes the following hypothesis:</div> <div dir="auto"> </div> <div dir="auto">Future scientific progress may require not only new mathematical frameworks, but also a higher level of precision in data representation.</div> <div dir="auto"> </div> <div dir="auto">If descriptions of objects and systems contain ambiguity, then any theoretical model built upon them inherits that ambiguity.</div> <div dir="auto"> </div> <div dir="auto">By reducing epistemic uncertainty, CTMinfo may provide a cleaner substrate for:</div> <div dir="auto"> </div> <div dir="auto">- detecting previously hidden patterns </div> <div dir="auto">- resolving apparent contradictions </div> <div dir="auto">- accelerating hypothesis testing </div> <div dir="auto"> </div> <div dir="auto">6. Implications</div> <div dir="auto">Potential implications include:</div> <div dir="auto"> </div> <div dir="auto">- increased efficiency in engineering and manufacturing systems </div> <div dir="auto">- improved interoperability between data systems </div> <div dir="auto">- reduction of errors in procurement and supply chains </div> <div dir="auto">- enhanced conditions for scientific discovery </div> <div dir="auto"> </div> <div dir="auto">Importantly, CTMinfo does not aim to replace existing scientific theories, but to improve the quality of data upon which they operate.</div> <div dir="auto"> </div> <div dir="auto">7. Limitations</div> <div dir="auto">The approach has defined limitations:</div> <div dir="auto"> </div> <div dir="auto">- currently applicable primarily to non-organic domains </div> <div dir="auto">- does not eliminate fundamental physical uncertainty </div> <div dir="auto">- does not replace theoretical physics or biological models </div> <div dir="auto"> </div> <div dir="auto">Its role is infrastructural rather than theoretical.</div> <div dir="auto"> </div> <div dir="auto">8. Conclusion</div> <div dir="auto">Scientific progress is traditionally associated with advances in theory and mathematics. However, the structure and quality of data may play an equally critical role.</div> <div dir="auto"> </div> <div dir="auto">Before redefining the laws of nature, it may be necessary to redefine how reality is described.</div> <div dir="auto"> </div> <div dir="auto">CTMinfo represents an attempt to address this foundational layer.</div> <div dir="auto"> </div> <div dir="auto">References</div> <div dir="auto"> </div> <div dir="auto">Heisenberg, W. (1927). On the quantum theoretical interpretation of kinematics and mechanics.</div> <div dir="auto"> </div> <div dir="auto">Kuhn, T. (1962). The Structure of Scientific Revolutions.</div> <div dir="auto"> </div> <div dir="auto">Wigner, E. (1960). The Unreasonable Effectiveness of Mathematics in the Natural Sciences.</div>