Towards Building an RDF-based Deep Document Model and Retrieval Augmented Generation System for Enhanced Question Answering with Large Language Models

Knowledge Graphs (KGs) are crucial for Retrieval-Augmented Generation (RAG), but traditional methods have limitations in capturing details and querying academic KGs. The challenges lie in identifying the appropriate KG type for RAG, such as a Metadata KG, and optimizing the integration of Large Language Models (LLMs) with KGs to enhance retrieval and generation. This paper introduces a novel framework combining the Deep Document Model (DDM) concept and a KG-enhanced Query Processing (KGQP) mechanism. DDM provides a comprehensive, hierarchical representation of academic papers using advanced Natural Language Processing (NLP) techniques, while KGQP optimizes complex queries using the KG’s structural information and semantic relationships. The framework also integrates KGs with state-of-the-art LLMs to improve knowledge utilization and downstream task performance. Evaluations show that the KG-based approach surpasses vector-based methods in relevance, accuracy, completeness, and readability. This research demonstrates the potential of combining KGs and LLMs for effective academic knowledge management and discovery. § Submission type: Poster §.