A Formal Framework for Structuring Knowledge into Executable Decision Objects
Abstract
This paper introduces an ontology-first approach to designing decision systems, where the fundamental unit is not a page, document, or tool, but a computational entity—a structured, executable representation of knowledge that integrates definitions, variables, constraints, transformations, and decision logic.
Traditional information systems separate content from computation, leading to fragmentation between knowledge representation and actionable outputs. We propose a unified framework in which ontological structures directly define computational behavior, enabling deterministic, auditable, and scalable decision processes.
The architecture formalizes entities as typed objects with embedded rules, enabling automatic generation of calculators, evaluators, and decision interfaces. This paradigm supports multi-domain applications, including financial modeling, engineering calculations, policy compliance, and AI-assisted decision systems.
1. Introduction
Most digital systems treat:
- Content as static text
- Computation as separate logic
This separation leads to inconsistency, duplication, lack of traceability, and weak decision reliability. We propose a paradigm shift:
From pages to computational entities
In this new paradigm, each unit of knowledge is structured, typed, executable, and composable.
2. Problem Statement
Let traditional systems be defined as:
System = Content + Code
With weak coupling between the two. We redefine the system as:
System = {E_1, E_2, ..., E_n}
Where each E_i is a computational entity:
E = (D, V, C, R, T, O)
Where:
- D: definitions
- V: variables
- C: constraints
- R: rules
- T: transformations
- O: outputs
3. Computational Entity Model
3.1 Formal Definition
A computational entity is a function:
E: (V, C, R) → O
subject to:
C(V) = true
and:
O = T(R(V))
3.2 Properties
Each entity must be deterministic, auditable, versioned, composable, and context-aware.
3.3 Entity Schema (Example)
{
"entity_id": "loan_payment",
"type": "financial_calculation",
"inputs": ["principal", "rate", "term"],
"constraints": ["principal > 0", "rate >= 0"],
"formula": "PMT = P * r / (1 - (1 + r)^-n)",
"outputs": ["monthly_payment"]
}
4. Ontology-First Design
4.1 Ontology Layer
This layer defines entities, relationships, types, and hierarchies.
Example:
FinancialEntity
├── Loan
├── Mortgage
└── Investment
4.2 Execution Layer
Transforms the ontology into calculators, decision engines, and evaluators.
4.3 Separation of Concerns
| Layer | Responsibility |
|---|---|
| Ontology | Meaning |
| Rules | Logic |
| Engine | Execution |
| Interface | Presentation |
5. Architecture
Figure 1 — Ontology-Driven System
Ontology Layer
↓
Entity Definitions
↓
Rule Engine
↓
Execution Engine
↓
Outputs (Calculators / Decisions)
Figure 2 — Entity Composition
Entity A (Inputs)
↓
Entity B (Transform)
↓
Entity C (Decision)
6. Decision Pipeline
We define a pipeline where each stage is governed by entity rules:
Input → Validation → Transformation → Evaluation → Output
7. Relation to Existing Paradigms
7.1 Knowledge Graphs
Represent relationships, but typically lack executable logic.
7.2 Rule-Based Systems
Executable, but often not semantically structured.
7.3 Machine Learning Systems
Predictive, but generally not interpretable.
7.4 Our Contribution
We unify ontology, rules, and execution into a single, cohesive structure.
8. Integration with Decision Systems
This framework integrates with Dual-Evidence Systems, scoring engines, and optimization pipelines. Each entity can independently produce features, evaluate constraints, and generate outputs.
9. Use Cases
- Financial Calculators: Loans, ROI, taxation.
- Engineering Systems: Structural calculations, safety checks.
- Policy Engines: Compliance, regulation.
- Decision Intelligence Platforms: Scenario simulation, optimization.
10. Formal Properties
10.1 Composability
E_3 = E_2(E_1(x))
10.2 Idempotence (Optional)
E(E(x)) = E(x)
10.3 Determinism
E(x) = y ⇒ E(x) = y
11. Limitations
- Ontology design complexity
- Rule explosion risk
- Need for strict governance
- Potential rigidity
12. Related Work
- Ontology Engineering: Gruber (1993) — Ontologies as specifications of conceptualizations.
- Knowledge Representation: RDF / OWL frameworks.
- Rule Systems: Expert systems, Prolog.
- Decision Systems: Decision theory, Multi-criteria decision analysis.
13. Future Work
- Automated ontology generation
- Integration with LLMs
- Hybrid symbolic and statistical systems
- Self-updating entities
14. Conclusion
We propose a shift:
From content-driven systems to ontology-driven computational systems
This enables unprecedented consistency, scalability, traceability, and decision reliability across complex domains.
References
- Gruber, T. (1993). A translation approach to portable ontology specifications. Knowledge Acquisition.
- Newell, A. (1982). The knowledge level. Artificial Intelligence.
- Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach. Pearson.
- Pearl, J. (2000). Causality: Models, Reasoning, and Inference. Cambridge University Press.