Conceptual Graph RAG Models for Complex Semantic Query Processing

This article introduces a new class of models for handling complex semantic queries-Conceptual Graph RAG Models-which combine the representational strengths of Conceptual Graphs (CG) with the dynamic retrieval capabilities of the Retrieval-Augmented Generation (RAG) paradigm. The paper examines two recent architectures, Graph RAG and G-Retriever, focusing on the extraction of relevant subgraphs, their optimal construction using Prize-Collecting Steiner Tree algorithms, and integration with large language models (LLMs) via soft prompt tuning. Methods such as Prompt Tuning and LoRA are discussed for enhancing efficiency while reducing the number of trainable parameters. A comprehensive review of evaluation metrics is presented, including precision/recall in retrieval, BERTScore, Mean Reciprocal Rank (MRR), Hop-Accuracy, and hallucination detection. Benchmark datasets such as PATQA, MINTQA, and WebQSP are also analyzed. The results show that the proposed approaches deliver high answer accuracy, improved interpretability, and greater robustness against misinformation-particularly when scaled to large knowledge graphs. The insights offered in this work will be of particular interest to researchers focused on ontology formalization and knowledge representation, especially those working at the intersection of symbolic reasoning and neural retrieval-generation systems. The topic also holds practical value for system architects and engineers of enterprise semantic platforms aiming to optimize and scale complex semantic queries by integrating graph-based knowledge representations with LLMs in intelligent data processing pipelines.