Constraint-Guided Enterprise Data Mapping with Large Language Models

• URL: http://arxiv.org/abs/2608.24218v1
• Date: Tue, 25 Aug 2026 08:23:45 GMT
• Title: Constraint-Guided Enterprise Data Mapping with Large Language Models
• Abstract summary: We propose a neuro-symbolic method with three stages: (i) schema-grounded admissibility constraints with metadata mc = tau_c, delta_c>, (ii) constraint-restricted candidate generation with cascade relaxation to guarantee a nonempty feasible set under noise, and (iii) neural ranking with bounded disambiguation restricted to that feasible set.<n> Methodologically, constraints operate as hypothesis-space operators rather than post-hoc validators, enabling controlled degradation under relaxation and auditable, human-guidable decisions.
• Score: 0.25453602861405583
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Enterprise entity alignment must handle semi-structured records, implicit attributes, and unit or granularity mismatches. Manual matching is still common in practice, but does not scale as schemas and providers evolve. LLM-only matching improves semantic recall, yet can violate structural and physical invariants, producing fluent yet operationally invalid correspondences. We propose constraint-guided mapping (CGM), a neuro-symbolic method with three stages: (i) schema-grounded admissibility constraints with metadata mc = <tau_c, delta_c>, where tau_c denotes the constraint type and delta_c provides executable relation and normalization logic; (ii) constraint-restricted candidate generation with cascade relaxation to guarantee a nonempty feasible set under noise; and (iii) neural ranking with bounded LLM disambiguation restricted to that feasible set. Methodologically, constraints operate as hypothesis-space operators rather than post-hoc validators, enabling controlled degradation under relaxation and auditable, human-guidable decisions. On a controlled structural-decoy benchmark, hard admissibility shrinks the candidate space by ~480x without dropping the GT, and a layer-by-layer ablation shows this gate, not the LLM, is the decisive lift (F1 0.08 to 0.66). The benefit is model-independent and adds no extra inference cost: a small model with constraints matches a frontier LLM used without them at ~28x lower cost. The method, not a single tuned configuration, transfers across seven enterprise makes (macro F1 0.70), each under its own automatically discovered, expert-refinable constraints, and lowers expert effort by ~7x versus spreadsheet workflows. Public Valentine results add an external ranking sanity check and mark the boundary: constraints should be hard only where structural invariants are match-determining.

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