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

该研究提出了一种基于实体建模的候选实体检索范式，该范式首先将 Wikidata 实体及其文本字段索引到文本搜索引擎中，再使用序列到序列模型生成目标实体的摘要，从而查询已索引的搜索引擎以检索候选实体，最后结合交叉注意力重新排序器，实现了在三个基于 Wikidata 的数据集上的最先进效果和在 TACKBP-2010 上的强劲表现的高度有效的混合方法候选实体检索和实体链接框架。