Ontology-Driven Structural Regularization for Document-Level Relation Extraction

2026-08-21

AI Summary

本文针对文档级关系抽取中的结构噪声问题，提出了一种本体驱动的框架来量化和加强数据集中的结构一致性，从而减少逻辑矛盾并提高模型性能。

Abstract

Document-Level Relation Extraction (DocRE) relies heavily on costly manually annotated datasets, while large distant supervision resources such as DocRED distant remain underexploited due to noise. We show that a critical yet overlooked source of noise lies in structural inconsistencies within relational triples, including violations of ontology constraints and logical contradictions. We introduce an ontology-driven framework to quantify and enforce structural consistency in DocRE datasets. Our analysis reveals substantial structural noise in DocRED distant and demonstrates that such inconsistencies propagate to model predictions. Enforcing structural well-formedness during training significantly reduces logical contradictions and consistently improves generalization performance. These findings establish structural consistency as a missing axis of supervision in DocRE and highlight structural regularization as an effective strategy for leveraging distant data at scale.

Problem

• Document-Level Relation Extraction
• structural inconsistencies
• ontology constraints
• logical contradictions

Innovation

• ontology-driven
• structural consistency
• document-level relation extraction
• structural regularization