Abstract
This publication discloses methods and systems for defining and applying explicit transformation declarations between independently maintained meaning environments, where each meaning environment governs locally-defined terms, type structures, and interpretation rules for a particular decision context. In one embodiment, a transformation declaration specifies mapping correspondences between meaning entries in a source meaning environment and meaning entries in a target meaning environment, and carries an information preservation classification indicating the nature and degree of information preservation achieved by the transformation. In one embodiment, transformation declarations may be composed sequentially to form transformation chains spanning multiple meaning environments, where the composed preservation classification reflects the weakest stage in the chain — the overall transformation is only as preserving as its least preserving component. In one embodiment, a transformation lineage record traces the history of transformations applied to a given piece of evidence, with per-stage uncertainty characterisation enabling downstream participants to assess the cumulative uncertainty introduced through the transformation chain. In one embodiment, cross- environment aggregation queries aggregate information from multiple meaning environments with explicit coverage tracking that records which environments could be projected and which could not, producing an exclusion list alongside projected results so that consumers can assess the completeness of the aggregation. The disclosed methods relate to problems addressed in measurement science as uncertainty propagation through instrument chains (GUM framework) and federated query processing in data mesh architectures where multi-source data collection must account for partial availability across autonomous participants. The disclosed approaches address the gap between existing schema mapping and ontology alignment techniques — which treat correspondences as technical integration artifacts without preservation metadata and do not account for semantic heterogeneity or lossy data transformation — and preservation-classified meaning transformation — which elevates the mapping to a governed artifact whose mapping fidelity and preservation metadata constrain what downstream operations the transformed result can support, enabling cross-jurisdictional data harmonization through data exchange format negotiation.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
Winchester, Jayson, "Schema Mapping — Fidelity-Tracked Transformation Between Independent Semantic Environments", Technical Disclosure Commons, (February 19, 2026)
https://www.tdcommons.org/dpubs_series/9346