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In our continuously evolving world, entities change over time and new,previously non-existing or unknown, entities appear. We study how thisevolutionary scenario impacts the performance on a well established entitylinking (EL) task. For that study, we introduce TempEL, an entity linkingdataset that consists of time-stratified English Wikipedia snapshots from 2013to 2022, from which we collect both anchor mentions of entities, and thesetarget entities' descriptions. By capturing such temporal aspects, our newlyintroduced TempEL resource contrasts with currently existing entity linkingdatasets, which are composed of fixed mentions linked to a single staticversion of a target Knowledge Base (e.g., Wikipedia 2010 for CoNLL-AIDA).Indeed, for each of our collected temporal snapshots, TempEL contains links toentities that are continual, i.e., occur in all of the years, as well ascompletely new entities that appear for the first time at some point. Thus, weenable 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 Basedescriptions as well as their mentions' contexts, and (ii) newly createdentities that were previously non-existing (e.g., at the time the EL model wastrained). Our experimental results show that in terms of temporal performancedegradation, (i) continual entities suffer a decrease of up to 3.1% ELaccuracy, while (ii) for new entities this accuracy drop is up to 17.9%. Thishighlights the challenge of the introduced TempEL dataset and opens newresearch prospects in the area of time-evolving entity disambiguation.

TempEL: Linking Dynamically Evolving and Newly Emerging Entities