Question Answering systems over Knowledge Graphs (KG) answer natural language questions using facts contained in a knowledge graph, and Simple Question Answering over Knowledge Graphs (KG-SimpleQA) means that the question can be answered by a single fact. Entity linking, which is a core component of KG-SimpleQA, detects the entities mentioned in questions, and links them to the actual entity in KG. However, traditional methods ignore some information of entities, especially entity types, which leads to the emergence of entity ambiguity problem. Besides, entity linking suffers from out-of-vocabulary (OOV) problem due to the limitation of pre-trained word embeddings. To address these problems, we encode questions in a novel way and encode the features contained in the entities in a multilevel way. To evaluate the enhancement of the whole KG-SimpleQA brought by our improved entity linking, we utilize a relatively simple approach for relation prediction. Besides, to reduce the impact of losing the feature during the encoding procedure, we utilize a ranking algorithm to re-rank (entity, relation) pairs. According to the experimental results, our method for entity linking achieves an accuracy of 81.8% that beats the state-of-the-art methods, and our improved entity linking brings a boost of 5.6% for the whole KG-SimpleQA.

中文翻译：

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改进的实体链接，用于知识图上的简单问答

基于知识图谱的问答系统 (KG) 使用知识图谱中包含的事实回答自然语言问题，而基于知识图谱的简单问答系统 (KG-SimpleQA) 意味着可以通过单个事实回答问题。实体链接是 KG-SimpleQA 的核心组件，它检测问题中提到的实体，并将它们链接到 KG 中的实际实体。然而，传统方法忽略了实体的一些信息，尤其是实体类型，从而导致了实体歧义问题的出现。此外，由于预训练词嵌入的限制，实体链接存在词汇外（OOV）问题。为了解决这些问题，我们以一种新颖的方式对问题进行编码，并以多层次的方式对实体中包含的特征进行编码。为了评估我们改进的实体链接带来的整个 KG-SimpleQA 的增强，我们使用了一种相对简单的关系预测方法。此外，为了减少在编码过程中丢失特征的影响，我们利用排序算法对（实体、关系）对进行重新排序。根据实验结果，我们的实体链接方法达到了 81.8% 的准确率，优于最先进的方法，我们改进的实体链接为整个 KG-SimpleQA 带来了 5.6% 的提升。