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Entity linking (EL) is the task of linking entity mentions in a document toreferent entities in a knowledge base (KB). Many previous studies focus onWikipedia-derived KBs. There is little work on EL over Wikidata, even though itis the most extensive crowdsourced KB. The scale of Wikidata can open up manynew real-world applications, but its massive number of entities also makes ELchallenging. To effectively narrow down the search space, we propose a novelcandidate retrieval paradigm based on entity profiling. Wikidata entities andtheir textual fields are first indexed into a text search engine (e.g.,Elasticsearch). During inference, given a mention and its context, we use asequence-to-sequence (seq2seq) model to generate the profile of the targetentity, which consists of its title and description. We use the profile toquery the indexed search engine to retrieve candidate entities. Our approachcomplements the traditional approach of using a Wikipedia anchor-textdictionary, enabling us to further design a highly effective hybrid method forcandidate retrieval. Combined with a simple cross-attention reranker, ourcomplete EL framework achieves state-of-the-art results on three Wikidata-baseddatasets and strong performance on TACKBP-2010.

Improving Candidate Retrieval with Entity Profile Generation for Wikidata Entity Linking