Rikishi: Reproducible operation and analysis of the Suggested Upper Merged Ontology
Year: 2026
Type: preprint
Abstract: The Suggested Upper Merged Ontology (SUMO) supplies general categories, relations and axioms for knowledge representation. Its source language, Standard Upper Ontology Knowledge Interchange Format (SUO-KIF), and its reasoning environment, Sigma Knowledge Engineering Environment (KEE), have different roles. Rikishi is a source-bound workbench for inspecting those commitments, submitting selected reasoning problems, and retaining their inputs and evidence. This paper asks how that infrastructure can make a scientific modeling connection inspectable. Prior Active Inference–SUMO alignment provides a direct precedent (Douglass et al., 2024; doi:10.5281/zenodo.11463326). One shared finite witness connects selected ontology expressions to probability models, numerical evaluation, pinned FEP_Formal Lean statements and synthetic observation histories. The ontology lane investigates declared model, representation, calculation and result roles. The mathematical lane supplies finite normalization, conditioning and information identities under stated hypotheses. The computational lane checks numerical outputs and generates trajectories from an explicit persistent-state model. Stable identifiers and common inputs join these lanes without treating their claims as interchangeable. Worked source formulas, direction-sensitive queries, numerical references and formal controls make the connections testable. Exact conditioning is separated from simulated frequencies, and variational inference from action selection and empirical model assessment. The resulting interface supports reproducible inspection of particular examples; it does not establish full-SUMO consistency, a general SUMO-to-Lean interpretation, verification of all Python execution, or biological validity of the Free Energy Principle (FEP).
Author: Friedman, Daniel Ari
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225414054