Hybrid Knowledge Graph and Large Language Model Architectures for Predictive Analytics

Abstract

Artificial Intelligence (AI) has significantly advanced predictive analytics across domains such as healthcare, finance, manufacturing, cybersecurity, and smart cities. While machine learning and deep learning models achieve strong predictive performance, they often lack structured knowledge integration and semantic reasoning. Knowledge Graphs (KGs) provide structured representations of entities and relationships but face challenges such as incomplete knowledge and limited adaptability. Conversely, Large Language Models (LLMs) offer powerful language understanding and contextual reasoning but may generate hallucinations and lack transparent reasoning. Hybrid Knowledge Graph–Large Language Model (KG–LLM) architectures address these limitations by combining symbolic reasoning with neural intelligence. This paper presents a comprehensive framework integrating graph embeddings, retrieval-augmented generation (RAG), transformer-based reasoning, attention mechanisms, and contextual embedding fusion to improve prediction accuracy, explainability, and robustness. The proposed approach supports applications including disease prediction, fraud detection, financial forecasting, predictive maintenance, customer analytics, and cybersecurity. Performance is evaluated using metrics such as Accuracy, Precision, Recall, F1-Score, AUC, and MAE, demonstrating superior results compared with standalone ML, KG, and LLM models. The study also discusses challenges, scalability, computational requirements, and future directions, including multimodal knowledge graphs, federated learning, explainable AI, and autonomous knowledge reasoning for trustworthy predictive analytics.