Beyond Ontology: Computational Epistemology as a Layered Inference Machine

Computational ontology has moved from classical knowledge representation into the center of enterprise artificial intelligence, as organizations seek governed semantic layers capable of reconciling fragmented data, objects, relationships, processes, and actions into a coherent digital representation. Yet ontological coherence does not by itself determine how a machine should infer under uncertainty. Once fragmented sources have been consolidated into a governed source of truth, a second and distinct question remains open: how should a system distinguish signal from noise, combine conflicting evidence, estimate event probabilities, and translate those probabilities into decision-relevant business impact. This paper introduces computational epistemology as an open, layered inference architecture built above a computational ontology. The framework formally defines the domain and decision problem, activates domain-specific inference engines drawn from statistics, stochastic processes, machine learning, symbolic logic, and fuzzy systems, and subjects every candidate signal to repeated evaluation across samples, windows, frequencies, specifications, and regimes. Within-family persistence and robustness are assessed before evidence is qualified; heterogeneous, sufficiently independent model families are then reconciled through cross-family convergence rather than simple agreement. Qualified evidence is synthesized probabilistically, stress-tested, and mapped through fuzzy membership before yielding a calibrated event probability, which is in turn associated with expected business impact through decision-impact models and returned as a decision-grade inference artifact. Automated feedback continuously recalibrates the architecture against realized outcomes, while a human modeler retains explicit governance over domain framing, admissible methods, and convergence criteria. The framework is open, domain-dependent, modeler-defined, methodologically plural, and automated in execution and recalibration, not a closed or universal algorithmic package. Throughout, additional architectural complexity is admitted only when it produces statistically and economically material gains over the simplest adequate baseline, making conditional complexity and business materiality organizing principles of the machine rather than afterthoughts.