Hybrid Knowledge Graph and Large Language Model Architectures for Predictive Analytics
TL;DR
This paper reviews hybrid KG–LLM frameworks for predictive analytics, highlighting graph embeddings, Retrieval-Augmented Generation (RAG), transformer-based reasoning, and contextual embedding fusion to improve prediction accuracy, interpretability, and robustness.
Abstract
Artificial Intelligence has significantly advanced predictive analytics, but traditional machine learning and deep learning models often struggle to integrate structured knowledge and provide explainable reasoning. Hybrid Knowledge Graph–Large Language Model (KG–LLM) architectures address these limitations by combining the structured semantic representation of Knowledge Graphs with the contextual reasoning capabilities of LLMs. This paper reviews hybrid KG–LLM frameworks for predictive analytics, highlighting graph embeddings, Retrieval-Augmented Generation (RAG), transformer-based reasoning, and contextual embedding fusion to improve prediction accuracy, interpretability, and robustness. The framework supports applications including healthcare, finance, cybersecurity, manufacturing, and customer analytics. Performance is evaluated using standard metrics such as Accuracy, Precision, Recall, F1-Score, AUC, and MAE, demonstrating superior results over standalone approaches. The paper also discusses scalability, computational challenges, explainable AI, federated learning, multimodal knowledge graphs, and future directions for trustworthy AI-driven predictive analytics.