International Journal of Science, Engineering and TechnologyAuthors: Nirmala S. Acharya
Abstract: Enterprise data management (EDM) has become a cornerstone of organizational efficiency, enabling timely and informed decision-making by consolidating, organizing, and analyzing vast volumes of heterogeneous data. However, traditional EDM approaches, including relational databases, data warehouses, and data lakes, often struggle to unify disparate data sources, maintain consistent quality, and provide actionable insights across complex enterprise environments. Knowledge graphs (KGs), with their ability to represent entities, relationships, and semantic context, have emerged as transformative tools for AI-driven EDM. By linking structured and unstructured data, KGs provide a rich foundation for machine learning and AI applications, enabling improved data discovery, inference, and predictive analytics. This review explores the influence of knowledge graphs on AI-driven enterprise data management, examining their concepts, construction methods, integration with AI, real-world applications, and associated challenges. The review highlights how KGs enhance semantic understanding, interoperability, and data governance while facilitating advanced AI techniques such as graph embeddings and graph neural networks. Limitations, including scalability, data quality, and maintenance challenges, are also discussed, alongside future directions such as automated KG construction, real-time updates, and hybrid cloud deployment. Overall, knowledge graphs represent a critical enabler for intelligent, AI-powered enterprise data management, offering a scalable, interpretable, and adaptive framework for modern organizations.
DOI: https://doi.org/10.5281/zenodo.17798606
