Résumé
In recent years, there has been a significant increase in the adoption of Large Language Models (LLMs) by users in both academic and industrial fields. These powerful tools are progressively challenging the dominance of traditional keyword-based search engines in various fields. While advancements like Retrieval-Augmented Generation (RAG) are enabling LLMs to provide provenance and explanations, their widespread adoption remains hindered by some well-known limitations, including hallucinations (factual inconsistencies), outdated knowledge, and answer precision. In contrast, classical search engines do not suffer from these issues, thanks to recent progress that has made them both efficient and accurate. However, their output may lack interpretability compared to LLMs. This vision paper proposes a novel explanation system that bridges this gap. By integrating Knowledge Graphs with RAG, we aim to elucidate the semantic relationships between retrieved resources and user queries. Addressing this research question has the potential to enhance user trust and confidence in the utilization of explainable search engines.