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

arXiv: https://arxiv.org/abs/2301.06974 · HTML: https://mlsanity.com/paper/2301.06974/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jan 2023.

## Built on by (most cited first, 2 of 2)

- [A Cross Attention Approach to Diagnostic Explainability using Clinical Practice Guidelines for Depression](https://mlsanity.com/paper/2311.13852/) (2023)
- [Extracting Document Relations from Search Corpus by Marginalizing over User Queries](https://mlsanity.com/paper/2507.10726/) (2025)

## Abstract (arXiv)

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.