Risk-Controlled Event-Driven Cascading Updates for Knowledge Graph Consistency Restoration

ACL ARR 2026 January Submission7664 Authors

Keywords: Information Extraction, Interpretability and Analysis of Models for NLP

Abstract:

Knowledge Graphs (KGs) provide structured and interpretable representations of real-world entities and relations. While dynamic KGs attempt to capture real-time changes, they typically treat updates as independent facts. This overlooks a critical challenge: a factual, localized update can contradict and invalidate previously correct knowledge, requiring revisions beyond the localized update to maintain KG consistency. Many of these inconsistencies arise from events whose effects propagate through relational dependencies, necessitating coordinated multi-hop reasoning rather than isolated changes. To address this, we introduce a model-agnostic framework for cascading KG update identification that leverages conformal prediction to provide reliable uncertainty guarantees over the cascade as a whole, accounting for dependencies among multi-hop update candidates. Building on this foundation, we further develop a graph-based KG update scoring framework that integrates large language models (LLMs) to enrich event representations with world knowledge. Experiments on two newly constructed real-world datasets, designed to reflect scenarios where events necessitate coordinated multi-hop updates, demonstrate that our framework establishes a strong baseline while offering calibrated confidence estimates,
providing an effective solution for event-driven KG consistency restoration.

Paper Type: Long

Research Area: Information Extraction and Retrieval

Research Area Keywords: Information Extraction,Interpretability and Analysis of Models for NLP

Contribution Types: NLP engineering experiment

Languages Studied: English

Submission Number: 7664