Negotiating Vocabularies at Link Time: A Deterministic Six-Class Compatibility Check for Federated Ontology Modules
Year: 2026
Type: preprint
Abstract: When two research programs use the same word for different concepts, or different words for the same concept, the incompatibility is usually discovered late, by a reader reconstructing it from prose. This paper transfers the link-time compatibility check compilers and package managers apply to code to scholarly vocabularies. If each work declares the terms it owns, imports, and refines as a content-addressed module, a federated linker classifies every cross-author interaction into exactly one of six classes, proposes a typed reconciliation in a standard mapping format, and fails a continuous-integration gate on the three unresolved classes, before either author reads the other's prose. The classification is a deterministic function of two parsed module sets grounded in content-addressed definition-hash identity; it occupies the zero-uncertainty corner of the matching space and adds the reconciliation and gate layer matching omits. The author implements the method as a dependency-light tool and evaluates it on five real, independent vocabulary pairs that exercise the full class set: a clean intra-author federation, an incumbent vocabulary yielding a dangling reference, a cross-disciplinary vocabulary yielding a same-key conflict, a large biomedical ontology yielding dangling references grounded in its own term-obsoletion governance, and a cultural-heritage standard yielding cross-domain reuse at volume. Includes zharnikov-2026at-negotiating-vocabularies-link.yaml (Paper Spec v0.1.0) – a machine-readable specification of the paper's claims, assumptions, and dependencies. The paper's full machine-first bundle (the SPINE claim/dependency graph and the ONTOLOGY term module) lives in the public repository; see https://github.com/spectralbranding/paper-spec for the standard. This PDF is generated programmatically from that machine-first source under a research-as-repository model.
Author: Zharnikov, Dmitry
Language: English
Topic: Semantic Web and Ontologies
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7165115142
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