Concept Taxonomy¶
12 categories covering all 496 concepts. No category exceeds 14% of total concepts.
| TaxonomyID | Category Name | Concepts | % | Description |
|---|---|---|---|---|
| GRAPH | Graph Fundamentals | 45 | 9.1% | Core graph theory: nodes, edges, LPG model, Cypher, graph algorithms, schema, serialization |
| EKG | Enterprise Knowledge Graphs | 30 | 6.0% | Building and scaling LPG-based knowledge graphs across enterprise systems |
| DATA | Data and Semantic Layers | 47 | 9.5% | Data lakes, semantic layers, metric stores, metadata management, and data governance |
| META | Metadata Standards | 28 | 5.6% | ISO 11179, metadata registries, data element definitions, NIEM, UMLS, thesauri |
| PROV | Process Mining and Provenance | 25 | 5.0% | Process mining, event logs, data lineage, data provenance, change data capture |
| CNTX | Context Problem and Context Graphs | 40 | 8.1% | The context problem, RAG limitations, context graph definition, schema, API, lifecycle |
| DTRC | Decision Traces and Graph Modeling | 66 | 13.3% | Decision trace anatomy, LPG implementation, incumbent challenges, graph data modeling |
| LLMI | LLM Foundations and Integration | 45 | 9.1% | Transformer architecture, prompting, fine-tuning, context retrieval, grounding strategies |
| BUILD | Building and Deploying Systems | 50 | 10.1% | Storage layers, ingestion pipelines, SDK, monitoring, AI agent architecture |
| USE | Enterprise Use Cases | 25 | 5.0% | Finance, sales, engineering, legal, HR, healthcare, and cross-domain applications |
| COMP | Compliance, Market, and Adoption | 55 | 11.1% | Audit trails, explainability, regulatory compliance, startup strategies, organizational adoption |
| INFRA | Data Infrastructure and Security | 40 | 8.1% | Data mesh, streaming platforms, vector databases, graph security, privacy, federated learning |
Category Definitions¶
GRAPH — Graph Fundamentals¶
Concepts: 1–25 (Topic 1) and 417–436 (cross-cutting graph theory)
The foundational building blocks of the Labeled Property Graph model and graph theory: nodes, edges, labels, properties, schemas, Cypher/openCypher/GQL query languages, graph algorithms (shortest path, PageRank, community detection), graph schema, serialization formats (GraphML, GraphSON), and the comparison of LPG with relational and RDF models including the scalability limitations of RDF.
EKG — Enterprise Knowledge Graphs¶
Concepts: 26–55 (Topic 2)
How organizations build and operate LPG-based knowledge graphs at enterprise scale: entity resolution, master data management, canonical entity models, hub-and-spoke and federated architectures, cross-system entity linking, ETL and ingestion patterns, graph sharding and replication, knowledge graph quality, ontologies, SKOS, and graph data catalogs.
DATA — Data and Semantic Layers¶
Concepts: 56–102 (Topics 3 and 4)
The infrastructure that gives enterprise data meaning: data lakes and lakehouses, semantic layers, metric stores, business glossaries, query federation, data virtualization, metadata management (technical, business, operational), data quality dimensions, data governance frameworks, stewardship, ownership, classification, access control, automated metadata discovery, and metadata catalog platforms.
META — Metadata Standards¶
Concepts: 103–130 (Topic 5)
Formal standards and vocabularies for enterprise metadata: the ISO/IEC 11179 metadata registry standard and its six core components (data element, data element concept, conceptual domain, value domain, object class, property), registration authorities, naming conventions, reference data management, code lists, metadata thesauri, UMLS, and NIEM.
PROV — Process Mining and Provenance¶
Concepts: 131–155 (Topic 6)
Reconstructing what actually happened from event logs and streams: process mining (process discovery, conformance checking, process enhancement), the IEEE XES standard, structured logging, upstream and downstream lineage, column-level lineage, data provenance, custody chains, transformation history, the OpenLineage standard, event sourcing, CQRS, append-only logs, change data capture, and temporal versioning.
CNTX — Context Problem and Context Graphs¶
Concepts: 156–195 (Topics 7 and 8)
The gap that context graphs fill: why LLMs fail at enterprise tasks even with existing infrastructure; RAG and its limits; tacit and implicit knowledge; context freshness, relevance, and poisoning; the definition of a context graph; how it extends rather than replaces the enterprise knowledge graph; context graph schema, API, read/write paths, lifecycle, and access control.
DTRC — Decision Traces and Graph Modeling¶
Concepts: 196–261 (Topics 9, 10, and 11)
The core of context graph content: the anatomy of a decision trace; exception logic, historical precedent, approval chains, and out-of-band approvals; LPG implementation patterns (decision trace node schema, edge types, trace-to-entity relationships, actor nodes, policy version edges, precedent chains); why incumbent systems will struggle to build this; and the full graph data modeling toolkit for context (bitemporal modeling, temporal edges, valid/transaction time, constraint enforcement, schema evolution).
LLMI — LLM Foundations and Integration¶
Concepts: 262–286 (Topic 12) and 457–476 (cross-cutting LLM foundations)
How large language models work and how they connect to context graphs: transformer architecture, tokens, prompting, fine-tuning, RLHF, in-context learning, zero- and few-shot prompting; context retrieval, relevance ranking, vector embeddings, hybrid retrieval, context window budgeting, compression and pruning, grounding strategies, hallucination mitigation, function calling, context graph tool definitions, and prompt injection risks.
BUILD — Building and Deploying Systems¶
Concepts: 287–336 (Topics 13 and 14)
End-to-end system construction: storage layer selection (property graph database, vector index, hybrid), ingestion pipelines (real-time, batch, backfill, event-driven), SDK/REST/GraphQL APIs, caching, replication, monitoring, alerting, SLAs, deployment patterns, testing strategies, observability, cost models; and the AI agent architecture layer: agent loop design, planning, memory, tool use, orchestration, multi-agent systems, graduated autonomy, human-in-the-loop, write-back, ReAct/Plan-and-Execute/ Reflection patterns, and agent evaluation.
USE — Enterprise Use Cases¶
Concepts: 337–361 (Topic 15)
Concrete applications of context graphs across the enterprise: finance (revenue reporting exceptions, ARR definition conflicts), sales (engagement history, playbook precedents), engineering (incident traces, production decision records), legal/compliance (regulatory audit automation), customer success (account history, escalation logic), HR, procurement, supply chain, marketing, risk, IT operations, healthcare, financial services, retail, manufacturing, insurance, government, and cross-department patterns.
COMP — Compliance, Market, and Adoption¶
Concepts: 362–416 (Topics 16, 17, and 18)
The organizational and regulatory context for context graphs: GDPR right to explanation, audit trail design, explainability by design vs. post-hoc, algorithmic accountability, bias/fairness audits, data retention and purge policies, compliance reporting, the EU AI Act; startup strategy (full replacement, module replacement, new system); beachhead workflow selection, glue functions, competitive moats; and the organizational adoption lifecycle (first workflow selection, change management, stakeholder alignment, ROI measurement, graduated autonomy rollout, center of excellence).
INFRA — Data Infrastructure and Security¶
Concepts: 437–456 (cross-cutting data engineering) and 477–496 (security and vector search)
Supporting infrastructure: data mesh, data products and contracts, data SLAs, data observability, SQL transformation tools, workflow orchestration, event streaming platforms, graph batch and streaming processing, multi-model and time-series integration, feature engineering from graphs; graph security models, row-level security, attribute-based access control, zero-trust architecture, data masking, anonymization, differential privacy, federated learning, model audit trails, AI red teaming; vector databases (HNSW, product quantization, ANN), embedding models, sentence transformers, dense and sparse retrieval (BM25), and context graph ROI modeling.