Aug 2026· American Journal of AI Digital Transformation and Regenerative Pharmacist· Vol 2, pp. 151-158· 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.
The increasing presence of heterogeneous data sources in modern information systems has intensified the need for intelligent data integration processes capable of handling semantic complexity, structural diversity, and dynamic changes. Traditional data integration methods, primarily based on relational schemas and syntactic mappings, struggle to address semantic heterogeneity in large-scale distributed environments. Knowledge graphs have emerged as a powerful paradigm, enabling semantically rich, flexible, and scalable integration by representing data as interconnected entities with metadata, ontologies, and inference capabilities. Using technologies such as RDF and OWL, knowledge graphs support interoperability, contextual reasoning, and unified data views across systems. This paper examines knowledge graph-based intelligent data integration systems, focusing on their architecture, methodology, and practical applications. It highlights their advantages in schema alignment, entity resolution, and semantic enrichment over traditional ETL approaches. The integration of machine learning techniques further enhances automation in data mapping, anomaly detection, and knowledge discovery. A systematic framework is proposed, covering ontology design, data ingestion, graph construction, and query optimization. A conceptual case study demonstrates improved integration accuracy, scalability, and query performance. Evaluation results indicate enhanced data quality, interoperability, and reasoning capabilities, along with reduced integration latency. Overall, knowledge graphs serve as a key enabler for next-generation intelligent data integration, supporting complex relationships and data-driven decision-making. Future work includes improving scalability, real-time processing, and integration with deep learning models.
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 management.
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 cases, conventional approaches to decision support lack deep semantic understanding, proper relationship modelling, explainability, and classification accuracy when dealing with enterprise data. To address these issues, this proposes a Hybrid Knowledge Graph and Transformer-Based Framework for Intelligent Decision Support in Enterprise Systems using the Knowledge Base Efficiency Evaluation Dataset. The framework begins with the implementation of the Data Entity Graph Building (DEGB) algorithm to pre-process data, extract entities, and build the relationship graph from the enterprise records. Furthermore, Multi Source Feature Integration Framework (MSFIF) then extracts semantic transformer features and graph-based embeddings to generate the integrated feature representation. Finally, Explainable Hybrid Decision Classification System (EHDCS) then performs enterprise classification and generates the positive or negative output with reasoning support for explainability. The proposed framework can achieve higher decision accuracy and learn better semantic relationships with more powerful feature representation capability and intelligent enterprise classification.
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 fail to capture complex relational dependencies and provide limited explainability for enterprise decision-making. This paper proposes an Explainable Graph Transformer Network (XGTN) for intelligent enterprise analytics and autonomous business process optimization. The proposed framework integrates Enterprise Knowledge Graphs with Graph Transformer Networks to model local and global dependencies using multihead self-attention. An explainability module combining attention visualization, SHAP, GNNExplainer, and counterfactual reasoning enhances transparency and trust in AIdriven predictions. Furthermore, an autonomous optimization engine supports workflow optimization, anomaly detection, resource allocation, and strategic decision-making. The framework comprises enterprise data integration, knowledge graph construction, graph transformer representation learning, explainability, autonomous optimization, and continuous feedback learning. Experimental results on benchmark enterprise datasets demonstrate that XGTN outperforms existing graph learning models in predictive accuracy, explainability, and scalability, providing a trustworthy and efficient solution for next-generation intelligent enterprise management systems.
Jagadeesh Mandala· International Conference Com...· 0 citations
Engineering Asset Management (EAM) is evolving through Industry 4.0 technologies such as IoT, AI, Digital Twins, and cloud computing, which generate vast amounts of asset-related data. However, conventional asset management systems often fail to integrate heterogeneous data sources, resulting in fragmented information, reactive maintenance, and increased operational costs. This study proposes a Knowledge Graph-Based Engineering Asset Management (KG-EAM) framework to enhance predictive decision intelligence for smart industrial assets. The framework integrates data from Industrial IoT devices, SCADA systems, Enterprise Asset Management (EAM) platforms, Computerized Maintenance Management Systems (CMMS), and engineering documentation into a unified semantic knowledge ecosystem. By combining ontology modeling, graph databases, graph embeddings, machine learning, and explainable AI (XAI), the framework enables predictive maintenance, root cause analysis, asset health prediction, and risk-aware decision-making. The proposed approach improves asset reliability, maintenance planning, operational efficiency, knowledge sharing, and lifecycle management while reducing downtime, maintenance costs, and unexpected equipment failures across diverse industrial sectors.
Vladimir Glushkov, Victor Glushkov· International Journal of Mod...· 0 citations
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