EMERGE: A Benchmark for Updating Knowledge Graphs with Emerging Textual Knowledge

TL;DR

This paper introduces EMERGE, a benchmark dataset for updating knowledge graphs with emerging textual knowledge, highlighting challenges and providing a resource for future research.

Contribution

It constructs a large-scale dataset of Wikidata snapshots and Wikipedia passages with corresponding KG edits to facilitate research on KG updating methods.

Findings

Identified key challenges in integrating textual knowledge with existing KGs.

Created a dataset with 233K passages and 1.45 million KG edits over 7 years.

Published the dataset and models for community use.

Abstract

Knowledge Graphs (KGs) are structured knowledge repositories containing entities and relations between them. In this paper, we study the problem of automatically updating KGs over time in response to evolving knowledge in unstructured textual sources. Addressing this problem requires identifying a wide range of update operations based on the state of an existing KG at a given time and the information extracted from text. This contrasts with traditional information extraction pipelines, which extract knowledge from text independently of the current state of a KG. To address this challenge, we propose a method for construction of a dataset consisting of Wikidata KG snapshots over time and Wikipedia passages paired with the corresponding edit operations that they induce in a particular KG snapshot. The resulting dataset comprises 233K Wikipedia passages aligned with a total of 1.45 million KG edits over 7 different yearly snapshots of Wikidata from 2019 to 2025. Our experimental results highlight key challenges in updating KG snapshots based on emerging textual knowledge, particularly in integrating knowledge expressed in text with the existing KG structure. These findings position the dataset as a valuable benchmark for future research. Our dataset and model implementations are publicly available.