Beyond Validation - A Conceptual Framework and Qualitative Review of AI-Driven Semantic Adjustment Mechanisms for Master Data Harmonization and Agricultural Process Data Integration

The integration of agricultural process data has long suffered from two intertwined yet methodologically distinct deficiencies: the inability of generic schema-level tools to resolve heterogeneous master data across proprietary systems, and the inability of formal validators to detect semantically implausible data that nevertheless conforms to the underlying schema. Both deficiencies demand not mere validation—the binary judgment of conformance—but active semantic adjustment: the contextual identification, resolution, and correction of meaning mismatches between systems. The ISO 11783-10 (ISOBUS) standard has demonstrated both limitations over more than two decades of field deployment. The new ISO 7673 series for agricultural field operations data, currently being finalised under ISO/TC 347, provides structural primitives such as the CompoundIdentifier, the ContextItem and the TimeScope, but does not address the algorithmic question of how those primitives are populated, validated, or maintained over time. This paper is conceptual in nature: it contributes a framework and a structured, qualitative review of candidate methodologies rather than empirical results. It frames the integration problem as two methodologically orthogonal sub-problems—master data harmonization (entity resolution) and process data plausibility (anomaly detection and adjustment)—and qualitatively assesses the methodological landscape across schema mapping, grammar-based fuzzing, fuzzy string matching, rule-based checks, statistical anomaly detection, deep learning entity resolution, pre-trained transformer embeddings, large language model approaches, and retrieval-augmented generation over knowledge graphs. The assessment draws on lessons from adjacent domains facing structurally analogous problems—fraud detection in banking and patient matching in healthcare—and explicitly weighs the interpretability of each method class, a decisive criterion for regulated compliance use cases. We argue that no single method addresses both problem classes and propose a hybrid, layered semantic adjustment architecture that orchestrates deterministic schema validation, statistical anomaly detection, embedding-based and LLM-based entity resolution, and graph-augmented contextual reasoning under an active-learning, audit-traceable governance layer. The mechanism is illustrated on two field-realistic use cases: cross-system master data matching for fields (land parcels), products, machines and operators; and the reconciliation of schema-conformant but semantically divergent implementation variants, illustrated by the heterogeneous conventions under which OEM systems transmit task totals. Beyond validation, the proposed architecture adjusts—it harmonizes, corrects, and enriches—transforming heterogeneous raw data into auditable, semantically coherent operational records.