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References: Enterprise Knowledge Graphs — Core Patterns

  1. Knowledge Graph - Wikipedia - Covers enterprise knowledge graph construction, entity types, relationship modeling, and applications at scale — foundational background for this chapter's treatment of domain subgraphs and canonical entity models across HR, finance, CRM, and ERP systems.

  2. Ontology (information science) - Wikipedia - Defines ontology as a formal representation of concepts, types, and relationships, directly supporting this chapter's contrast of ontologies vs. taxonomies and the role of ontologies as schema backbones for enterprise knowledge graphs.

  3. Entity Resolution - Wikipedia - Explains deterministic and probabilistic approaches to matching records across systems to a single canonical entity — the foundational challenge this chapter identifies as the prerequisite for all enterprise knowledge graph construction.

  4. Knowledge Graphs: Fundamentals, Techniques, and Applications - Aidan Hogan et al. - MIT Press - Chapters 3–5 cover knowledge graph construction from heterogeneous enterprise sources, entity resolution, schema alignment, and federated graph architectures at the technical depth this chapter introduces.

  5. Graph Databases (2nd ed.) - Ian Robinson, Jim Webber, Emil Eifrem - O'Reilly Media - Chapters 4–6 cover enterprise graph deployment patterns including hub-and-spoke federation, ETL ingestion patterns, schema governance, and scaling to large graphs — directly matching this chapter's core topics.

  6. Enterprise Knowledge Graph - Wikipedia - Surveys enterprise knowledge graph implementations, their integration patterns with source systems, and the organizational challenges of maintaining cross-domain canonical entity models described in this chapter.

  7. SKOS Simple Knowledge Organization System - W3C - Official W3C reference for SKOS, the standard for representing taxonomies and controlled vocabularies in machine-readable form — directly relevant to this chapter's section on SKOS as a bridge between taxonomies and graph ontologies.

  8. OpenLineage Open Standard - OpenLineage Project - Documents the open standard for capturing pipeline lineage and provenance metadata — supporting this chapter's treatment of missing provenance as an operational failure mode and governed ingestion as a pipeline requirement.

  9. Data Lineage - Wikipedia - Covers lineage tracking across ETL pipelines and data systems, supporting this chapter's five-stage graph ingestion pattern and the requirement to record provenance metadata at every load step.

  10. Graph Theory - Wikipedia - Covers foundational graph theory including graph partitioning and clustering algorithms relevant to this chapter's discussion of graph sharding strategies that minimize cross-shard edges in billion-edge enterprise deployments.