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.

翻译：知识图谱（KG）是包含实体及其关系的结构化知识库。本文研究如何根据非结构化文本源中不断演变的知识，自动随时间更新知识图谱。解决该问题需要基于现有知识图谱在特定时刻的状态与文本中提取的信息，识别多种更新操作。这与传统信息抽取流程不同——后者独立于知识图谱当前状态从文本中提取知识。为应对这一挑战，我们提出了一种数据集构建方法，该方法包含维基数据（Wikidata）知识图谱随时间变化的快照，以及维基百科篇章与相应编辑操作的配对（这些操作由特定知识图谱快照中的篇章引发）。最终数据集包含23.3万段维基百科文章，对应2019年至2025年7个不同年份的维基数据快照中共计145万条知识图谱编辑记录。实验结果表明，基于新兴文本知识更新知识图谱快照面临关键挑战，尤其体现在如何将文本表达的知识与现有知识图谱结构进行整合。这些发现使该数据集成为未来研究的宝贵基准。我们的数据集与模型实现均已公开。