@inproceedings{ni-etal-2026-risk,
title = "Risk-Controlled Event-Driven Cascading Updates for Knowledge Graph Consistency Restoration",
author = "Ni, Bo and
Ge, Qinwen and
Fu, Haowei and
Rossi, Ryan A. and
Liu, Xiaorui and
Xu, Jiejun and
Derr, Tyler",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.2111/",
pages = "42534--42548",
ISBN = "979-8-89176-395-1",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T Risk-Controlled Event-Driven Cascading Updates for Knowledge Graph Consistency Restoration
%A Ni, Bo
%A Ge, Qinwen
%A Fu, Haowei
%A Rossi, Ryan A.
%A Liu, Xiaorui
%A Xu, Jiejun
%A Derr, Tyler
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F ni-etal-2026-risk
%X 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.
%U https://aclanthology.org/2026.findings-acl.2111/
%P 42534-42548
Markdown (Informal)
[Risk-Controlled Event-Driven Cascading Updates for Knowledge Graph Consistency Restoration](https://aclanthology.org/2026.findings-acl.2111/) (Ni et al., Findings 2026)
ACL
- Bo Ni, Qinwen Ge, Haowei Fu, Ryan A. Rossi, Xiaorui Liu, Jiejun Xu, and Tyler Derr. 2026. Risk-Controlled Event-Driven Cascading Updates for Knowledge Graph Consistency Restoration. In Findings of the Association for Computational Linguistics: ACL 2026, pages 42534–42548, San Diego, California, United States. Association for Computational Linguistics.