Exploring the Integration of Knowledge Graphs in RAG for Enhanced Question Answering

The chapter delves into the potential benefits of incorporating knowledge graphs into Retrieval Augmented Generation (RAG) for improved question answering, highlighting the scarcity of focus in the information retrieval community. It discusses the complexities in search systems, the role of LAMs in user experience enhancement, and the challenges in using Large Language Models (LLMs) for data extraction. The chapter also explores the acquisition of projects by major companies, the success of older approaches versus LLMs, and the future implications of automated knowledge graph construction in ranking systems.