Meaning shifts with context. The framework must shift with it.
The Multi-Ontology Lifecycle Framework (MOLF) is the applied implementation of Relational Dynamics Theory. Where RDT describes the substrate (self-reference, oscillation, phase-aligned energy redistribution), MOLF operationalises it: a data architecture in which multiple classification systems coexist, interact, and evolve dynamically within a single relational field.
Traditional approaches to data classification treat information domains as static and independent. Cost sits in one system. Risk sits in another. Carbon sits in a third. Each operates on its own ontology. Crosswalks between them are manual, brittle, and lag behind reality. The result is a fragmented view of any complex system, where the interdependencies that actually drive behaviour are invisible.
MOLF dissolves this by treating every ontology as a dimension in a multidimensional field. Changes in one dimension propagate through influence coefficients to all others. The system expresses interdependence in real time. A price movement in steel simultaneously affects cost classification, carbon impact, procurement strategy, and stakeholder sentiment, and the framework makes those connections visible, measurable, and auditable.
MOLF is governed by five principles, each derived from the Seven Tenets and from the mathematical structure of RDT.
A term's meaning depends on its relationships to other terms, across ontologies and across lifecycle stages. "Risk" under one contract type is "opportunity" under another. "Carbon saving" in design becomes "cost premium" in construction. MOLF encodes these relationships as dynamic influence coefficients, not static crosswalks.
When a value changes in one ontology, the effects ripple through all connected ontologies. MOLF models this propagation explicitly through a relational tensor that stores the weighted influence between every pair of terms across every dimension. Nothing acts in isolation. Every node both affects and is affected by others.
The framework computes a single coherence metric across the entire relational field:
High coherence indicates that the system's ontologies are in alignment: the data is telling a consistent story. Low coherence reveals emerging conflict, drift, or instability. When coherence drops below a threshold, it signals the need for recalibration or human review. Coherence is a leading indicator. It moves before harm materialises.
Every label assignment is resolved dynamically based on lifecycle stage, policy weights, and organisational context. The same data object can carry different meanings in different phases (concept, definition, procurement, delivery, operation, legacy) without contradiction, because MOLF tracks meaning as a function of position, not as a fixed property.
Every influence pathway, every propagation, every coherence shift is traceable. When a decision is made, the relational field that informed it is preserved. This enables not only accountability but learning: the organisation can see how systemic tensions resolved, which ontologies drove the outcome, and where the model's predictions matched or diverged from reality.
In practice, MOLF transforms how organisations navigate complexity. It enables a live, truthful view of system dynamics by unifying previously siloed data domains into a relational continuum. Specifically:
Anticipatory governance. Coherence degradation is detected before it manifests as cost overrun, schedule delay, or reputational harm. The system moves from reactive reporting to early-warning intelligence.
Cross-domain optimisation. Because ontologies interact within a single field, trade-offs between cost, time, sustainability, risk, and stakeholder sentiment become visible and navigable. Decisions are no longer made in isolation of their obscured impacts.
Uncertainty as relation. Traditional uncertainty modelling treats variance as randomness. MOLF treats it as relational responsiveness: the degree to which a system's nodes are coupled and the sensitivity of that coupling to perturbation. This is a more faithful depiction of how complex systems actually behave.
Scalable integration. MOLF can be implemented in relational databases or graph stores, integrating with existing data environments. It does not replace existing ontologies. It connects them.
MOLF is not a separate framework that happens to reference RDT. It is RDT expressed in the language of data architecture. The relational tensor is the oscillatory field. The influence coefficients are the phase alignments. The coherence metric is the conservation constraint. The context resolver is observer-dependent measurement. The lifecycle stages are positions on the manifold.
This means that every prediction RDT makes about oscillatory systems applies to MOLF implementations. Coherence will oscillate. Phase transitions will occur (moments where the system reorganises rapidly). Feedback loops will self-correct toward equilibrium. And the Stem Process will describe the change dynamics at every level.
The theory and the tool are the same object. One describes the territory. The other navigates it.
MOLF is a proprietary methodology.
The principles above are shared openly to establish the framework's foundations and its connection to RDT. The full technical methodology, including the relational tensor architecture, implementation schema, influence coefficient calibration, and deployment guidance, is available under licence.
Contact Melanie Phillips for implementation enquiries.