From Macro-Ontology to Atomic Formula: The Parametric and Granular Decomposition of Knowledge in the UMOKWAI Epistemic-Operational Model
From Macro-Ontology to Atomic Formula: The Parametric and Granular Decomposition of Knowledge in the UMOKWAI Epistemic-Operational ModelCorpus reference: Formal extension of the UMOSU ontology and the UMOKWAI epistemic-operational model (Zenodo, DOI: 10.5281/zenodo.22365912)Bibliographic classification systems and classical formal ontologies (BFO, DOLCE) offer categorical structures useful for the macro-organization of knowledge, but do not resolve the deterministic validation of the single scientific assertion. This work formalizes UMOKWAI's transition from a classification model to a deterministic framework for the indexing, verification, and dynamic management of knowledge (anti-hallucination), proposing a recursive, parametric decomposition architecture across 5 levels (L0–L4): from the Fundamental Epistemic Class (K1–K8) down to the Atomic Formula (a single equation, norm, or proposition that cannot be further divided).The document also introduces the cascading selective-activation mechanism (macro routing → domain specialization → atomic validation) and the management of the temporal currency of assertions (:ActiveStatus / :ObsoleteStatus / :supersededBy), applied to three domains: hard sciences (K1–K5), law (K8), and economics (K7), with the aim of neutralizing the probabilistic inertia of large language models with respect to superseded theories, rulings lacking currently valid normative coverage, and historically superseded economic regimes.KeywordsUMOSU, UMOKWAI, knowledge ontology, formal epistemology, anti-hallucination, deterministic validation, artificial intelligence, knowledge classification, atomic formula, probabilistic inertia
Authors
- Marco Falsetti (ORCID: https://orcid.org/0000-0002-0483-9212)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-09-25
- DOI
- https://doi.org/10.5281/zenodo.22955875
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- preprint