Thanks to recent advancements in machine learning, vector-based methods have been adopted in many modern information retrieval (IR) systems. While showing promising retrieval performance, these approaches typically fail to explain why a particular document is retrieved as a query result to address explainable information retrieval(XIR). Knowledge graphs record structured information about entities and inherently explainable relationships. Most of existing XIR approaches focus exclusively on the retrieval model with little consideration on using existing knowledge graphs for providing an explanation. In this paper, we propose a general architecture to incorporate knowledge graphs for XIR in various steps of the retrieval process. Furthermore, we create two instances of the architecture for different types of explanation. We evaluate our approaches on well-known IR benchmarks using standard metrics and compare them with vector-based methods as baselines.

翻译：由于在机器学习方面最近取得的进展,许多现代信息检索系统采用了基于病媒的方法。这些方法虽然显示了有希望的检索性能,但通常无法解释为什么将某一文件作为查询结果检索,以解决可解释的信息检索(XIR)问题。知识图表记录了关于实体和内在可解释关系的结构化信息。现有的XIR方法大多完全侧重于检索模式,很少考虑利用现有的知识图来解释。在本文件中,我们提议了一个将XIR知识图纳入检索过程各个步骤的一般结构。此外,我们创建了两种不同解释类型的结构实例。我们使用标准衡量标准来评估我们关于众所周知的IR基准的方法,并将它们与基于病媒的方法作为基准进行比较。