Improving Candidate Retrieval with Entity Profile Generation for W ikidata Entity Linking (2022)

Abstract: 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.

## introduction entity linking (el) is the task of mapping entity mentions in a document to standard referent entities in a target knowledge base (kb) @xcite @xcite @xcite @xcite . el systems have found applications in many tasks such as question answering @xcite , knowledge base population @xcite , given the importance of el, researchers have introduced a plethora of el methods, ranging from using hand-crafted features @xcite to using deep language models @xcite @xcite . the vast majority of these studies have focused on linking mentions to wikipedia or wikipedia-derived kbs such as dbpedia @xcite or yago @xcite . as of november 2021, there are about 6.4 million articles in english wikipedia. however, many entities are still missing from wikipedia @xcite . on the other hand, wikidata, the most extensive general-interest kb, has much broader coverage than wikipedia @xcite . wikidata contains more than 40 million entities with english titles, about seven times more than the number of articles in english wikipedia. every entity in wikipedia has an equivalent entry in wikidata, but not vice versa. the scale of wikidata can open up many new real-world applications. when a disaster happens, many people rush to social media to share updates about the event @xcite . using an el system to extract critical information (e.g., affected locations and donor agencies) can aid in monitoring the situation @xcite . however, many entities may not be wellknown, and these entities are likely to be present in wikidata than in wikipedia @xcite . despite the potential of wikidata becoming a universal hub of real-world entities, there exists little in-depth research on el over wikidata @xcite . the massive number of entities in wikidata makes it challenging to find the correct entity for an input mention. many previous el methods for wikipedia use a dictionary built from anchor texts to reduce the original search space to a small list of candidate entities @xcite @xcite . this dictionary-based approach is not directly applicable to wikidata, since the description of each entity in wikidata does not contain any anchor text. in this work, we propose a novel candidate retrieval paradigm for wikidata based on entity profile generation. wikidata entities and their textual fields are first indexed into a text search engine (e.g., elasticsearch). given an entity mention and its context, we use a seq2seq model to generate the profile of the target entity, which consists of its title and description. the profile is then used to query the indexed search engine to retrieve candidate entities. our technique is applicable to virtually any kb, not just wikipedia or wikidata. it also complements the dictionary-based approach, enabling us to further design an effective hybrid method for candidate retrieval. combined with a simple cross-attention reranker, our complete el framework achieves state-of-the-art (sota) results on three wikidata-based datasets and strong performance on the standard tackbp-2010 dataset. in summary, our main contributions are: (1) a novel candidate retrieval paradigm based on entity profiling and (2) a new el framework for wikidata. extensive experiments on four public datasets verify the effectiveness of our framework. we refer to our framework as epgel, which stands for entity profile generation for entity linking. ## conclusions and future work this paper has proposed a novel profiling-based paradigm to candidate retrieval for el. the tech-nique is highly generalizable and complementary to the traditional dictionary-based approach, enabling the design of an effective hybrid candidate retrieval method. together with a cross-attention reranker, our complete el framework achieves strong performance on four public datasets. we plan to explore a broader range of properties and information about the target entity that can be extracted from the mention's context. for example, type-based features can be helpful for el @xcite ; as such, we aim to make our generation model generate the target entity's type. also, in this work, we use a local model for candidate reranking. we plan to explore the use of a more global model for collective el @xcite @xcite .