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Knowledge Graph–Driven Enterprise Data Integration for Autonomous Decision Intelligence

Aug 2026 · American Journal of AI Digital Transformation and Regenerative Pharmacist · 0 citations · 23 references

Abstract

Modern enterprises generate vast amounts of data from diverse sources, including business applications, cloud platforms, IoT devices, social networks, and transactional systems. Integrating and analyzing this heterogeneous data efficiently remains a significant challenge due to data silos, semantic inconsistencies, and complex relationships among entities. Knowledge Graphs (KGs) have emerged as a powerful technology for representing interconnected enterprise data through semantic relationships, enabling enhanced data integration, contextual understanding, and intelligent knowledge discovery. This paper presents a Knowledge Graph–Driven Enterprise Data Integration Framework for Autonomous Decision Intelligence that unifies heterogeneous data sources into a semantically enriched knowledge ecosystem. The proposed framework employs ontology modeling, entity resolution, semantic mapping, graph construction, and intelligent reasoning mechanisms to establish meaningful relationships among enterprise data assets. Advanced graph analytics and machine learning techniques are integrated to support autonomous decision-making by generating contextual insights, identifying hidden patterns, and providing real-time recommendations. The framework further incorporates automated data governance, metadata management, and explainable reasoning capabilities to ensure data quality, transparency, and regulatory compliance. Experimental evaluation demonstrates that the proposed approach significantly improves data integration accuracy, knowledge discovery efficiency, and decision intelligence performance compared with traditional data integration systems. By leveraging knowledge graphs and intelligent reasoning engines, the framework enables organizations to transform fragmented enterprise data into actionable knowledge, thereby enhancing operational efficiency, strategic planning, and autonomous business decision-making. The proposed solution provides a scalable and intelligent foundation for next-generation enterprise analytics and AIdriven decision support systems.

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