Semantic collision reduction
This expertise axis aims to prevent abusive fusions, identity shifts, and association drifts between entities, pages, and sources, when inference systems construct plausible but erroneous links.
A semantic collision occurs when two distinct entities become partially interchangeable in the interpretation space: mixed attributes, fused roles, capabilities attributed to the wrong perimeter.
This axis is attached to AI disambiguation and relies on interpretive governance.
Problem
Modern systems operate by proximity: co-occurrences, thematic similarities, lexical neighborhoods, and partial source overlaps. When perimeters and exclusions are not declared, these proximities can become fusions.
The problem reinforces itself through iteration: an erroneous association repeated progressively becomes “plausible”, then stabilizes, for lack of explicit constraints.
Typical consequences
- Fusion of a person and a brand, or a concept and a product.
- Attribution of services or capabilities to the wrong perimeter.
- Divergent descriptions depending on the assistants and systems consulted.
- Propagation of an erroneous association in secondary sources.
- Difficulty correcting an error once it has been copied.
Conceptual levers
- Explicit exclusions: non-equivalence declarations and inference limits.
- Source hierarchy: stable priority of truth points.
- Canonical relations: directional references that reduce interchangeability.
- Entity graph: structured articulation of real links, without extrapolation.
- Controlled redundancy: inter-surface coherence to prevent drifts.
Canonical references
Back to the map: Expertise.
