论文标题
知识图嵌入和可解释的AI
Knowledge Graph Embeddings and Explainable AI
论文作者
论文摘要
现在，知识图嵌入是一种广泛采用的知识表示方法，其中实体和关系嵌入了向量空间中。在本章中，我们通过解释它们是什么，如何生成以及如何评估它们来介绍读者的知识图嵌入概念。我们通过描述已引入的方法来代表向量空间中的知识来总结该领域的最新方法。关于知识表示，我们考虑了解释性的问题，并讨论了通过知识图嵌入获得的预测的模型和方法。
Knowledge graph embeddings are now a widely adopted approach to knowledge representation in which entities and relationships are embedded in vector spaces. In this chapter, we introduce the reader to the concept of knowledge graph embeddings by explaining what they are, how they can be generated and how they can be evaluated. We summarize the state-of-the-art in this field by describing the approaches that have been introduced to represent knowledge in the vector space. In relation to knowledge representation, we consider the problem of explainability, and discuss models and methods for explaining predictions obtained via knowledge graph embeddings.
