Format-Constraint Coupling in Knowledge Graph Construction from Statistical Tables

2026-05-21

AI Summary

This study addresses a critical yet previously unrecognized issue in knowledge graph construction: the coupling between tabular serialization formats and schema constraints, which significantly degrades both factual coverage and graph fidelity—particularly in country-year statistical tables, where it induces entity inflation or extraction failure. The authors formally identify and name this phenomenon “format-constraint coupling,” introduce a direct graph access evaluation paradigm, and release CSVFidelity-Bench, a benchmark comprising diverse table types and gold-standard facts. Through factorial experiments, bootstrap confidence intervals, token ablation studies, and multi-LLM comparisons, they uncover significant positive coupling effects in four out of six datasets (peak effect size +1.180). Direct graph access reveals a quality gap as large as 47.6 percentage points (p<0.0001), substantially exceeding that of standard retrieval-based approaches.

Abstract

An extraction schema should not reduce knowledge graph fidelity. On statistical CSV, however, it can. We study country-by-year time-series matrices, a common layout on open-data portals. In this setting, serialization format and schema constraints interact super-additively. Their joint effect exceeds the sum of independent effects by up to +1.180 (2x2 factorial, 6 datasets). Bootstrap 95% CIs are strictly positive on 4/6 datasets, with strongest evidence on wide Type-II matrices. More critically, a schema applied to a mismatched format can trigger catastrophic mismatch. Fact coverage falls below the unconstrained baseline on 4/6 datasets through entity inflation or extraction refusal. We call this observed pattern format-constraint coupling. Probing and token ablation support a surface-form anchoring explanation centred on column-name references. Controlled variants across format-schema pairings, GraphRAG hosts, and LLM families show the same direction within the measured scope; one LLM family shows only partial activation. The observation also has a diagnostic consequence. Three standard retrieval modes largely mask construction quality (delta <= 1pp), whereas direct graph access exposes gaps up to +47.6pp (p < 0.0001). To support fidelity-aware evaluation, we release CSVFidelity-Bench. It contains 15 datasets, 11 Type-II matrices, 4 Type-III tables, and 1,892 Gold Standard facts across 6 domains.

Problem

• format-constraint coupling
• knowledge graph fidelity
• statistical tables
• schema constraints
• entity inflation

Innovation

• format-constraint coupling
• knowledge graph construction
• statistical tables
• schema fidelity
• surface-form anchoring