As AI systems take on roles demanding interpretability and explainability (especially with deep learning and structured reasoning blending) we need knowledge modeling that is both human-intuitive and machine-operable. This plan proposes a model-driven approach centered on Object Role Modeling (ORM) as a core semantic interface for LLM-centric and neuro-symbolic systems.
Role-based semantic core: Conceptual abstraction with constraint primacy and higher-arity relationships.
Interoperable outputs: High-fidelity JSON; precise FOL; verbalizations for transparency.
Neuro-symbolic orchestration: Two-way flow between neural prediction and symbolic validation.
Domain reach: Finance, manufacturing, legal, and more; precision with flexible design.
High-Level Overview
Problem Space and Market Context
LLMs’ limits: Reliability, logical consistency, and interpretability issues; lack explicit world models.
Symbolic rigidity: Formal systems are precise but hard to scale and inaccessible to many experts.
Legacy constraints: Existing conceptual/semantic approaches lack composability and tooling depth.
Relational resurgence: DuckDB and specialized systems show the power of lean, embedded analytics.
This plan proposes a modeling-first, role-based framework that unifies conceptual rigor with LLM-enabled orchestration—bridging neural prediction and symbolic verification.
Mission, Vision, and Value Proposition
Mission
Empower humans and AI with an expressive, role-based semantic modeling framework that connects symbolic reasoning and neural inference, guided by a practical formalist definition of ontology.
Vision
Natural, precise modeling via roles, constraints, and verbalizations.
Hybrid inference with verifiability and utility.
Continuous evolution of models alongside data and conversation.
Core Value Proposition
Domain Experts: Natural modeling + verbalized explanations; no heavy syntax.
AI Engineers: JSON + FOL + pluggable symbolic/neural flows.
Product Teams: Rapid, explainable systems across high-stakes domains.
AI Systems: Live semantic backbone for structure, inference, and guardrails.
Self-reflective LLMs using verbalizations/FOL constraints.
Alignment and guardrails via role-based models.
Structured world models powering memory and reasoning.
Future Enhancements (Examples)
JSON-LD compatibility for Linked Data.
Conceptual Graphs translation.
ORM-driven prompt compiler.
ORM-Agent integration (semantic core in agents).
Conclusion and Next Steps
The ORM Toolkit aims to serve as a modeling-first semantic backbone for neuro-symbolic AI, uniting clarity and precision of logic with the power of neural models.
Next Steps
Finalize MVP specs and design.
Select Year 1 pilot use case.
Open collaboration channels (forum, GitHub).
Establish partnerships with symbolic and LLM orchestration teams.