Improving Entity Disambiguation by Reasoning over a Knowledge Base (2022)

Abstract: Recent work in entity disambiguation (ED) has typically neglected structured knowledge base (KB) facts, and instead relied on a limited subset of KB information, such as entity descriptions or types. This limits the range of contexts in which entities can be disambiguated. To allow the use of all KB facts, as well as descriptions and types, we introduce an ED model which links entities by reasoning over a symbolic knowledge base in a fully differentiable fashion. Our model surpasses state-of-the-art baselines on six well-established ED datasets by 1.3 F1 on average. By allowing access to all KB information, our model is less reliant on popularity-based entity priors, and improves performance on the challenging ShadowLink dataset (which emphasises infrequent and ambiguous entities) by 12.7 F1.

## introduction entity disambiguation (ed) is the task of linking mentions of entities in text documents to their corresponding entities in a knowledge base (kb). recent ed models typically use a small subset of kb information (such as entity types or descriptions) to perform linking. these models have strong performance on standard ed datasets, which consist mostly of entities that appear frequently in the training data. however, ed performance deteriorates for less common entities, to the extent that many recent models are outperformed by outdated feature engineering-based ed systems on datasets that focus on challenging or rare entities @xcite . this suggests models over-rely on prior probabilities, which are either implicitly learned or provided as features, rather than make effective use of the mention context. one reason for this is that the subset of kb information used by the models is not enough to discriminate between * tom and joseph contributed equally to this work. similar entities in all contexts, meaning the model has to fall back on predicting the most popular entity. another explanation for the performance drop is that less common entities are prone to missing or inconsistent kb information (e.g. they may not have a description), which is problematic for models which rely on a single source of information. to illustrate, we find that 21% of the 25% least popular foot_0 entities in wikidata have neither an english description nor any entity type foot_1 , leaving no mechanism for models which rely on these two sources of information alone to disambiguate them (other than their label). 3 over half of these entities have at least one kb fact (e.g. [cafe gratitude], [headquarters location], [san francisco]); so by including kb facts the percentage of the least popular entities with no information aside from a label drops from 21% to 8%. in light of this, we introduce an ed model which has access to entity types and descriptions, and all kb facts. by using a larger variety of information, our model is more robust to missing kb information, and is able to disambiguate entities in a broader range of contexts without relying on entity priors. figure 1 shows an example sentence where there is insufficient information in the entity descriptions and types to disambiguate the mention, clinton. fine-grained kb information, such as facts about the birthplace or education of candidate entities, is required. to incorporate kb facts, our model begins by reranking candidate entities using descriptions @xcite and predicted entity types @xcite . we then predict, using the document context, the relations which exist between every pair of mentions in the document. for example, given the sentence in figure 1 , the model may predict that the [place of birth] relation exists between the mention clinton and the mention hope, arkansas. 4 for this, we introduce a novel "coarseto-fine" document-level relation extraction (re) module, which increases accuracy and reduces inference time relative to the standard re approach. given the relation predictions, we query the kb (wikidata in our case) for facts which exist between any of the candidate entities for the mention clinton and for the mention hope, arkansas. in this case we would find the wikidata fact [bill clinton], [place of birth], [hope], and would correspondingly boost the scores of both the [bill clinton] and [hope] entities. we implement this mechanism with the kb stored in a one-hot encoded sparse tensor, which makes the architecture end-to-end differentiable. our model surpasses state-of-the-art (sota) baselines on well-established ed datasets by 1.3 f1 on average, and significantly improves performance on the challenging shadowlink dataset by 12.7 f1. in addition, the model predictions are interpretable, in that the facts used by the model to make predictions are accessible. our contributions are summarised as follows: ## related work recent work on ed has primarily focused on feature-based approaches, whereby a neural network is optimised so that the representation of the correct kb entity is most similar to the mention representation, and each mention is resolved independently. the way in which the kb entities are represented varies between work. initial work @xcite learned entity embeddings directly from training examples, which performed well for entities seen during training, but could not resolve unseen entities. more recent work improved performance on common datasets by enabling linking to entities unseen during training by using a subset of kb information to represent entities, such as entity descriptions @xcite or entity types @xcite . ## analysis 6.1 relation predictions to understand the relations which the model utilises to make predictions, the recall numbers appear low, although this is expected behaviour in that the existence of a gold fact does not necessarily imply that the text in the document infers this fact. for example, the text "donald trump visited new york" would include the gold fact [donald trump] [place of birth] [new york] but making this prediction for all sentences of this form would likely harm performance. ## conclusion we presented a novel ed model, which achieves sota performance on well-established ed datasets by a margin of 1.3 f1 on average, and by 12.7 f1 on the challenging shadowlink dataset. these results were achieved by introducing a method to incorporate large symbolic kb data into an ed model in a fully differentiable and scalable fashion. our analysis shows that better candidategeneration strategies are an interesting avenue for future research, if results are to be pushed higher on ambiguous and tail entities. dynamic expansion of the kb by incorporating facts identified by the ed model is also a potentially promising direction.