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.

中文翻译：

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LNN-EL：短文本实体链接的神经符号方法

实体链接 (EL) 是通过将文本中的提及项链接到知识图中的实体来消除它们的歧义的任务，对于文本理解、问答或对话系统至关重要。由于上下文有限，短文本（例如，单个句子或问题）上的实体链接带来了特殊的挑战。虽然先前的方法使用启发式或黑盒神经方法，但在这里我们提出了 LNN-EL，这是一种神经符号方法，它将使用基于一阶逻辑的可解释规则的优点与神经学习的性能相结合。尽管受限于使用规则，LNN-EL 与 SotA 黑盒神经方法相比具有竞争力，并具有可扩展性和可转移性的额外优势。特别是，我们表明我们可以轻松地混合人类专家给出的现有规则模板，具有多种类型的特征（先验、BERT 编码、框嵌入等），甚至是先前 EL 方法产生的分数，从而改进了这些方法。例如，在 LC-QuAD-1.0 数据集上，我们显示 F1 分数比之前的 SotA 增加了 4 美元以上。最后，我们展示了使用逻辑提供的归纳偏差导致学习规则可以在数据集之间很好地转移，即使没有微调，同时保持高精度。