Hybrid Knowledge Graph and Large Language Model Architectures for Predictive Analytics

  • Authors

    • Dr. Meena Krishnan Assistant Professor, Anna University, India. Author

    DOI:

    https://doi.org/10.67228/3142788X/IJMLPA-2025PII2J5L

    Published 12-04-2025

  • Hybrid Artificial Intelligence, Knowledge Graphs, Large Language Models, Predictive Analytics, Graph Neural Networks, Semantic Reasoning, Retrieval-Augmented Generation (RAG), Explainable Artificial Intelligence (XAI), Transformer Models, Machine Learning, Deep Learning, Graph Embeddings, Contextual Intelligence, Decision Support Systems

    Issue

    Vol. 8 No. 2 (2025)

    Section

    Articles

    How to Cite

    [1]
    M. Krishnan, “Hybrid Knowledge Graph and Large Language Model Architectures for Predictive Analytics”, IJMLPA, vol. 8, no. 2, pp. 01–17, Dec. 2025, doi: 10.67228/3142788X/IJMLPA-2025PII2J5L.

  • 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.

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