Retrieval-Augmented Scientific Reasoning Framework Using Knowledge Graphs And Large Language Models

Abstract

The proliferation of scientific literature exponentially has rendered it more challenging to researchers to effectively access pertinent information, combine evidence of various sources, and produce credible scientific findings. Despite the fact that Retrieval-Augmented Generation (RAG) has made significant strides in enhancing the capacity of Large Language Models (LLMs) by accessing external knowledge when generating responses, the current methods are commonly constrained by disjointed document retrieval, poor use of organized scientific knowledge, poor multi-hop reasoning, and hallucinated outputs that undermine the quality of scientific insights generated. To resolve these issues, this paper introduces a Knowledge Graph-Driven Scientific Retrieval-Augmented Generation (KG-SRAG) system that uses knowledge graphs regarding scientific knowledge and hybrid retrieval and big language models to enable accurate and explainable scientific reasoning. The proposed Hybrid Evidence Retrieval and Multi-Hop Explainable Scientific Reasoning (HERMES) algorithm fuels the framework and combines dense semantic retrieval, sparse lexical retrieval, evidence exploration through a knowledge graph, and multi-hop reasoning to infer and synthesize the most relevant scientific evidence. Moreover, a mathematical optimization model with many objectives is presented aiming to maximize the relevance of retrieval, consistency of graphs, confidence in reasoning and efficiency in computations and minimize response latency. The suggested framework is assessed using benchmark scientific reasoning data sets, and compared with representative retrieval-augmented and knowledge graph-based methods. Experimental findings show enhanced retrieval performance, increased accuracy of reasoning, better grounding of evidence and less hallucination as compared to current methods. Evidence verification and confidence estimation can also enhance transparency and interpretability of generated responses; the combination of structured scientific knowledge and retrieval-augmented large language models does so as well. The suggested framework offers a powerful and reliable intelligent method of scientific literature analysis, automated evidence synthesis and AI-assisted research support, offering a novel retrieval-enhanced scientific reasoner architecture, an explainable hybrid reasoner algorithm and an optimization-based framework of next-generation scientific knowledge discovery.