Entity linking (EL), the task of disambiguating mentions in text by linking
them to entities in a knowledge graph, is crucial for text understanding,
question answering or conversational systems. Entity linking on short text
(e.g., single sentence or question) poses particular challenges due to limited
context. While prior approaches use either heuristics or black-box neural
methods, here we propose LNN-EL, a neuro-symbolic approach that combines the
advantages of using interpretable rules based on first-order logic with the
performance of neural learning. Even though constrained to using rules, LNN-EL
performs competitively against SotA black-box neural approaches, with the added
benefits of extensibility and transferability. In particular, we show that we
can easily blend existing rule templates given by a human expert, with multiple
types of features (priors, BERT encodings, box embeddings, etc), and even
scores resulting from previous EL methods, thus improving on such methods. For
instance, on the LC-QuAD-1.0 dataset, we show more than $4$\% increase in F1
score over previous SotA. Finally, we show that the inductive bias offered by
using logic results in learned rules that transfer well across datasets, even
without fine tuning, while maintaining high accuracy.

本文提出了一种名为 LNN-EL 的神经符号方法，通过结合适用于一阶逻辑的可解释规则的优势和神经学习的性能，对短文本中的实体链接问题进行了处理。此方法不仅可以与基于规则的传统方法相媲美，而且还具有可扩展性和可转移性，并且可以跨数据集进行学习。