Format-Constraint Coupling in Knowledge Graph Construction from Statistical Tables

2025-03-15 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, extended

Summary

Research on knowledge graph construction from matrix-layout statistical CSV tables reveals "format-constraint coupling," where serialization format and extraction schema interact super-additively. This joint effect can exceed independent contributions by up to +1.180, with bootstrap 95% CIs strictly positive on 4/6 datasets, particularly wide Type-II matrices. Critically, applying a schema to a mismatched format can trigger catastrophic failures, causing fact coverage to fall below the unconstrained baseline on 4/6 datasets through entity inflation or extraction refusal. The observed pattern is explained by surface-form anchoring, centered on column-name references. While standard retrieval modes largely mask these construction quality issues (Δ≤1pp), direct graph access exposes significant gaps up to +47.6pp. To support fidelity-aware evaluation, the CSVFidelity-Bench is released, comprising 15 datasets, 11 Type-II matrices, 4 Type-III tables, and 1,892 Gold Standard facts across 6 domains.

Key takeaway

For Machine Learning Engineers building knowledge graphs from statistical CSVs, you must prioritize format-schema alignment. Mismatched schemas can catastrophically reduce fact coverage, often performing worse than no schema at all. Use deterministic parsers for small, regular, or purely numeric tables. For wide matrices with descriptive columns (CDS≥0.02, TTF≥0.4), LLM extraction is beneficial, but only with a carefully matched format-schema pairing. Crucially, evaluate graph construction fidelity directly, as end-to-end QA often masks underlying binding failures.

Key insights

Knowledge graph fidelity from statistical tables critically depends on aligning serialization format with extraction schema, as mismatches cause catastrophic failures.

Principles

• Format-schema alignment is super-additive.
• Mismatched schemas degrade fact coverage.
• Column-name references anchor extraction.

Method

A three-stage apparatus classifies CSV topology, induces an extraction schema from column metadata, then serializes input and injects the schema into the LLM prompt to ensure anchorable row-level structure.

In practice

• Use deterministic parsing for small, regular CSVs.
• Apply LLM extraction to wide, descriptive matrices.
• Co-design serialization format and schema.

Topics

• Knowledge Graph Construction
• Statistical Tables
• LLM Extraction
• Schema-Guided Extraction
• Data Serialization
• GraphRAG Systems

Code references

• jxnl/instructor

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.