In our continuously evolving world, entities change over time and new, previously non-existing or unknown, entities appear. We study how this evolutionary scenario impacts the performance on a well established entity linking (EL) task. For that study, we introduce TempEL, an entity linking dataset that consists of time-stratified English Wikipedia snapshots from 2013 to 2022, from which we collect both anchor mentions of entities, and these target entities' descriptions. By capturing such temporal aspects, our newly introduced TempEL resource contrasts with currently existing entity linking datasets, which are composed of fixed mentions linked to a single static version of a target Knowledge Base (e.g., Wikipedia 2010 for CoNLL-AIDA). Indeed, for each of our collected temporal snapshots, TempEL contains links to entities that are continual, i.e., occur in all of the years, as well as completely new entities that appear for the first time at some point. Thus, we enable to quantify the performance of current state-of-the-art EL models for: (i) entities that are subject to changes over time in their Knowledge Base descriptions as well as their mentions' contexts, and (ii) newly created entities that were previously non-existing (e.g., at the time the EL model was trained). Our experimental results show that in terms of temporal performance degradation, (i) continual entities suffer a decrease of up to 3.1% EL accuracy, while (ii) for new entities this accuracy drop is up to 17.9%. This highlights the challenge of the introduced TempEL dataset and opens new research prospects in the area of time-evolving entity disambiguation.

翻译：在持续演变的世界中，实体随时间而变化，新的、此前不存在或未知的实体不断涌现。我们研究了这种演化过程如何影响已成熟的实体链接任务性能。为此，我们引入TempEL数据集，该数据集由2013年至2022年间按时间分层的英文维基百科快照组成，从中收集了实体的锚点提及以及这些目标实体的描述。通过捕捉这些时间维度，我们新构建的TempEL资源与现有实体链接数据集形成鲜明对比——后者由固定提及链接到单一静态版本的目标知识库（例如CoNLL-AIDA中的维基百科2010版本）。具体而言，在每个收集的时间快照中，TempEL既包含持续存在的实体（即每年均出现的实体），也包括首次出现的全新实体。因此，我们能够量化当前最先进的实体链接模型在以下两类场景中的性能：（i）知识库描述及其提及上下文随时间变化的实体，以及（ii）此前不存在的全新创建实体（例如在实体链接模型训练时尚未出现）。实验结果表明，在时间性能退化方面，（i）持续实体的实体链接准确率下降高达3.1%，而（ii）新实体的准确率下降幅度可达17.9%。这凸显了我们引入的TempEL数据集的挑战性，并为时间演化型实体消歧领域开辟了新的研究前景。