Transforming AI Decision Support System with Knowledge Graphs & CAG
DOI:
https://doi.org/10.63503/j.ijaimd.2025.110Keywords:
Artificial Intelligence (AI), Decision Support Systems (DSS), Knowledge Graphs (KGs), Context-Aware Graphs (CAGs), Large Language Models (LLMs), Semantic Embeddings, Information Retrieval, Business Intelligence, Machine LearningAbstract
Artificial Intelligence (AI) serves as a fundamental component of decision support systems (DSS), enabling organizations to process large-scale data and derive actionable insights. However, traditional AI models utilizing relational databases (RDBMS) exhibit limitations in retaining context and applying knowledge-driven reasoning. This study examines the integration of Knowledge Graphs (KGs) and Context-Aware Graphs (CAGs) to enhance AI-driven decision-making systems. A hybrid framework is proposed in which structured knowledge graphs improve the contextual understanding of large language models (LLMs), thereby optimizing information retrieval, similarity-based search, and multi-query handling. The system employs semantic embeddings to map entities and relationships, utilizing Neo4j and machine learning techniques to enhance inference capabilities. A comparative analysis with conventional RDBMS-based AI models demonstrates significant improvements in query accuracy, explainability, and relevance for decision-making tasks.
The proposed approach is evaluated in various domains, including business intelligence, financial analysis, and strategic policymaking. Results indicate that KGs and CAGs enable organizations to obtain more reliable, transparent, and context-aware insights. Additionally, user feedback mechanisms are incorporated to dynamically refine the knowledge graph, ensuring continuous enhancement of AI responses. By bridging structured data with generative AI, this research contributes to the advancement of decision support systems, predictive analytics, and expert recommendation frameworks. The findings suggest that knowledge-enhanced AI models substantially outperform traditional methods in contextual reasoning and decision optimization, offering a scalable and explainable AI framework for enterprise applications. This approach ensures adaptability in AI-driven decision systems by facilitating continuous learning from emerging data trends, thereby enabling more intelligent and data-informed business strategies.
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