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Chapters

This textbook is organized into 22 chapters covering 496 concepts.

Chapters are sequenced so that every concept's prerequisites appear in an equal or earlier chapter — the order is a valid topological sort of the full learning graph.

Chapter Overview

  1. Knowledge Graphs and Labeled Property Graphs — Introduces the Labeled Property Graph model — nodes, edges, labels, properties, Cypher — and explains why LPGs are the dominant enterprise choice over RDF and relational models.
  2. Semantic Layers for Data Lakes — Covers the data infrastructure that gives enterprise data meaning: data lakehouses, semantic layers, metric stores, query federation, data virtualization, and governance frameworks.
  3. Metadata Management — Examines technical and business metadata, quality dimensions, data stewardship, access control, and the differential-privacy and federated-learning techniques that underpin policy enforcement.
  4. Enterprise Knowledge Graphs — Core Patterns — Explains how organizations build and operate LPG-based knowledge graphs at scale: canonical entity models, hub-and-spoke federation, ETL ingestion patterns, and graph sharding.
  5. Graph Theory, Algorithms, and Advanced Enterprise KG — Covers graph algorithms (PageRank, shortest path, community detection, graph clustering) and the advanced entity-resolution and information-extraction patterns that build on both graph and metadata foundations.
  6. Metadata Registries and ISO 11179 — Explores formal standards for enterprise metadata: the six ISO 11179 components, registration authorities, naming conventions, code lists, reference data management, UMLS, and NIEM.
  7. Process Mining, Data Lineage, and Provenance — Reconstructs what actually happened from event logs using IEEE XES, process discovery, conformance checking, OpenLineage, column-level lineage, event sourcing, and change data capture.
  8. The Context Problem and RAG Limitations — Makes the case for context graphs by explaining why LLMs fail at enterprise tasks even with RAG: tacit knowledge gaps, context freshness, relevance limits, and context poisoning.
  9. What a Context Graph Is — Defines context graphs — how they extend enterprise knowledge graphs, their node-and-edge schema, API, read/write paths, lifecycle management, and access-control model.
  10. LLM and AI Foundations — Covers transformer architecture, tokenization, prompting, fine-tuning, RLHF, in-context learning, zero/few-shot prompting, and LLM function calling to build the AI literacy needed for integration chapters.
  11. Decision Traces: Anatomy and LPG Patterns — Dissects the decision trace — exception logic, historical precedents, approval chains, out-of-band approvals — and shows the complete LPG node schema and edge-type vocabulary for implementing them.
  12. Incumbent Challenges in Building Context Systems — Analyzes why existing BI tools, CRMs, ERP systems, and document stores will structurally struggle to capture and serve decision traces as context.
  13. Graph Data Modeling for Context — Presents the graph data modeling toolkit for context: bitemporal modeling, valid and transaction time, temporal edges, constraint enforcement, schema evolution, and multi-version patterns.
  14. Integrating LLMs with Context Graphs — Shows how context graphs wire into LLM workflows: context retrieval, relevance ranking, hybrid retrieval combining dense and sparse (BM25) search, context window budgeting, grounding, and hallucination mitigation.
  15. Building and Deploying Context Graph Systems — Covers end-to-end system construction: storage layer selection, real-time and batch ingestion pipelines, SDK/REST/GraphQL APIs, caching, replication, monitoring, SLAs, and testing strategies.
  16. AI Agent Architecture — Covers agent loop design, planning, memory, tool use, multi-agent orchestration, graduated autonomy, human-in-the-loop, write-back, and the ReAct/Plan-and-Execute/Reflection agent patterns.
  17. Enterprise Use Cases — Demonstrates concrete applications of context graphs across finance, sales, engineering, legal, HR, healthcare, procurement, and risk management domains.
  18. Compliance, Explainability, and Audit — Covers GDPR right to explanation, audit trail design, algorithmic accountability, bias audits, data retention and purge policies, the EU AI Act, and AI red teaming for deployed systems.
  19. Market Strategy and Startup Approaches — Analyzes the competitive landscape and startup strategy: full replacement vs. module replacement vs. new system, beachhead workflow selection, glue functions, and competitive moats.
  20. Organizational Adoption and Governance — Covers the organizational adoption lifecycle: first workflow selection, change management, stakeholder alignment, ROI measurement, graduated autonomy rollout, and building a center of excellence.
  21. Data Engineering and Infrastructure — Supporting infrastructure: data mesh, streaming platforms, graph batch and streaming processing, workflow orchestration, SQL transformation, feature engineering from graphs, and context graph observability.
  22. Security, Privacy, and Vector Search — Covers graph security (row-level security, zero-trust, ABAC), vector databases (HNSW, product quantization, ANN), dense and sparse retrieval, embedding model selection, and the context graph ROI model.

How to Use This Textbook

Each chapter lists its prerequisite chapters at the top of its index page. Foundational readers should progress linearly; experienced practitioners can use the learning graph viewer to identify exactly which earlier concepts they need before jumping into a later chapter.

Note: Each chapter index includes the full concept list and a "TODO: Generate Chapter Content" marker for the content-generation skill.