论文标题
端到端实体链接和歧义词和知识图嵌入
End-to-End Entity Linking and Disambiguation leveraging Word and Knowledge Graph Embeddings
论文作者
论文摘要
实体链接 - 连接实体在自然语言语言中提到知识图（kg）实体是对kgs回答问题的关键步骤。它通常基于测量实体标签之间的字符串相似性及其在问题中的提及。问题中提到的关系可以有助于用相同的标签在实体之间消除歧义。如果在关系链接步骤中确定了不正确的关系，这可能会误导。但是，错误的关系在语义上仍然与正确的实体在kg中形成三倍的关系。可以通过其kg嵌入的相似性来捕获。基于这个想法，我们提出了一种使用KG以及单词嵌入来执行简单问题的联合关系和实体分类的第一种端到端神经网络方法，同时借助一种新型的控球机制，在隐式执行实体歧义。经验评估表明，所提出的方法实现了与最先进的实体联系的性能，同时需要减少后处理。
Entity linking - connecting entity mentions in a natural language utterance to knowledge graph (KG) entities is a crucial step for question answering over KGs. It is often based on measuring the string similarity between the entity label and its mention in the question. The relation referred to in the question can help to disambiguate between entities with the same label. This can be misleading if an incorrect relation has been identified in the relation linking step. However, an incorrect relation may still be semantically similar to the relation in which the correct entity forms a triple within the KG; which could be captured by the similarity of their KG embeddings. Based on this idea, we propose the first end-to-end neural network approach that employs KG as well as word embeddings to perform joint relation and entity classification of simple questions while implicitly performing entity disambiguation with the help of a novel gating mechanism. An empirical evaluation shows that the proposed approach achieves a performance comparable to state-of-the-art entity linking while requiring less post-processing.
