Aug 2026· American Journal of AI Digital Transformation and Regenerative Pharmacist· 0 citations· 23 references
TL;DR
A Knowledge Graph–Driven Enterprise Data Integration Framework for Autonomous Decision Intelligence that unifies heterogeneous data sources into a semantically enriched knowledge ecosystem and provides a scalable and intelligent foundation for next-generation enterprise analytics and AIdriven decision support systems is presented.
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.
Examination of knowledge graph-based intelligent data integration systems highlights their advantages in schema alignment, entity resolution, and semantic enrichment over traditional ETL approaches, and proposes a systematic framework covering ontology design, data ingestion, graph construction, and query optimization.
Muhammad Al-Azar· International Journal of App...· 0 citations
Experimental evaluation demonstrates improved relationship discovery, query performance, and knowledge extraction compared with conventional relational approaches, making the proposed framework suitable for intelligent applications in healthcare, cybersecurity, finance, smart manufacturing, and enterprise knowledge man...
Mahabala H. N.· International Journal of Dat...· 0 citations
Enterprise decision support systems are a type of intelligent computing system that processes and analyses the enterprise data to make accurate decisions. Typically, machine learning and data analytics techniques are used to analyze enterprise data that are structured and unstructured for decision support. In many case...
M. Kota· International Conference Com...· 0 citations
Experimental results show that AI-driven knowledge graphs significantly enhance decision accuracy, reduce ambiguity, and improve interpretability, achieving up to 85–92% higher decision efficiency compared to traditional methods.
Venkatesh Iyer, Nandhini Ravi· International Journal of Art...· 0 citations
Modern enterprises generate massive volumes of heterogeneous and interconnected data from Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), Supply Chain Management (SCM), Internet of Things (IoT), financial systems, and external business sources. Traditional machine learning approaches often f...
Jagadeesh Mandala· International Conference Com...· 0 citations
A Knowledge Graph-Based Engineering Asset Management (KG-EAM) framework to enhance predictive decision intelligence for smart industrial assets while reducing downtime, maintenance costs, and unexpected equipment failures across diverse industrial sectors is proposed.
Vladimir Glushkov, Victor Glushkov· International Journal of Mod...· 0 citations
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