Enterprise information is distributed across applications, data platforms, documents, metadata repositories, process models, and architecture tools, making relationship-centric analysis difficult. This paper proposes an Enterprise Knowledge Graph Architecture that connects business entities, semantics, metadata, lineage, processes, policies, technologies, risks, and events into a governed relationship layer. The framework integrates ontology management, entity resolution, graph storage, data ingestion, provenance, security, query services, graph analytics, SAP Business Data Cloud, SAP Datasphere, SAP HANA Cloud, SAP LeanIX, SAP Signavio, Retrieval-Augmented Generation, and Artificial Intelligence. It explains how Knowledge Graphs support enterprise search, architecture impact analysis, data discovery, decision intelligence, AI agents, and graph-enhanced RAG while preserving ownership, access control, semantic quality, and lifecycle governance. The proposed architecture enables organizations to transform fragmented information into reusable Business Knowledge and improve contextual intelligence across enterprise domains.

Sanjeeve Kumar Gajadi, "Enterprise Knowledge Graph Architecture" International Journal of Scientific Research and Engineering Development, V9(5): Page(550-553) September - October 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.