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

Towards Improving the Explainability of Text-based Information Retrieval with Knowledge Graphs