Skip to content

From Macro-Ontology to Atomic Formula: The Parametric and Granular Decomposition of Knowledge in the UMOKWAI Epistemic-Operational Model

Sep 2026 · Zenodo (CERN European Organization for Nuclear Research)

Abstract

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and economics (K7), with the aim of neutralizing the probabilistic inertia of large language models with respect to superseded theories, rulings lacking currently valid normative coverage, and historically superseded economic regimes.KeywordsUMOSU, UMOKWAI, knowledge ontology, formal epistemology, anti-hallucination, deterministic validation, artificial intelligence,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

View source

Similar papers

#artificial intelligence Conference Open access Jun 2018

Gibson Env: Real-World Perception for Embodied Agents

This paper investigates developing real-world perception for active agents, proposes Gibson Environment for this purpose, and showcases a set of perceptual tasks learned therein.

F. Xia, Amir Zamir, Zhi-Yang He et al. · 1,069 citations · ⚡112
#artificial intelligence Open access May 2023

Evaluating the Performance of Large Language Models on GAOKAO Benchmark

GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...

Xiaotian Zhang, Chun-yan Li, Yi Zong et al. · 216 citations · ⚡17

KTO: Model Alignment as Prospect Theoretic Optimization

This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.

Kawin Ethayarajh, Winnie Xu, Niklas Muennighoff et al. · 195 citations · ⚡28
#computer vision Open access Jun 2016

Software Development in Startup Companies: The Greenfield Startup Model

The results are packaged in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible, and the need to shorten time-to-market, by speeding up the development through low-precision engineering activities.

Carmine Giardino, Nicolò Paternoster, M. Unterkalmsteiner et al. · 178 citations · ⚡14
#computer vision Open access Oct 2016

Software Startups - A Research Agenda

Software startup companies develop innovative, software-intensive products within limited timeframes and with few resources, searching for sustainable and scalable business models.

M. Unterkalmsteiner, P. Abrahamsson, Xiaofeng Wang et al. · 157 citations · ⚡17

Related blog posts

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.