Graph Engineering

Graph engineering is the discipline of designing the structures agents work through — not the prompts. It has two halves:

1. Knowledge graphs — what agents remember. Nodes are entities and facts, edges are relationships with time and provenance. This file's 9-stage pipeline covers it, distilled from Southeast University's graduate KG course (https://github.com/npubird/KnowledgeGraphCourse, Prof. Peng Wang), translated to English and adapted for LLM-era agents.
2. Task graphs — how agents work. Nodes are jobs, edges are execution dependencies: parallel fan-out, separate verifier contexts, the stop rule, the human gate. Read references/task-graphs.md when the request is about orchestrating agents rather than building memory.

Core mental model: a knowledge graph is a product with a schema, not a pile of triples. Quality comes from the pipeline order — model the domain BEFORE extracting, fuse BEFORE storing, evaluate at every stage.

Teaching Mode

When the user wants to LEARN graph engineering (rather than build something), teach it — do not just execute. Rules:

1. Anchor every stage in the user's own domain: ask for one real project or dataset, then use it as the running example through all stages.
2. Generate visual artifacts as you teach. Concepts in this discipline are shapes; show them. For each major concept, produce a small diagram the user can keep — mermaid diagrams (flowchart for the pipeline and task graphs, graph LR for example ontologies and subgraphs) or a single self-contained HTML page when interactivity helps. At minimum: the 9-stage pipeline, a 3-type ontology drawn from the user's domain, one extracted subgraph (5-10 nodes) from a real sample, and the diamond pattern with the user's own jobs as nodes.
3. Teach in the pipeline's order, one stage per exchange, each ending with a small exercise ("write 3 competency questions for your project") before moving on.
4. Close by assembling what was built during the lesson into a starter ontology.yaml and a drawn task graph for the user's first real build.

The 9-Stage Pipeline

Run stages in order. For small projects stages 4-6 collapse into one extraction pass, but never skip stages 3 (ontology) or 8 (fusion) — they are where real-world graphs fail.

1. Scope & value test — Confirm a graph beats a simpler structure. A graph pays off when queries are multi-hop ("who worked with X on projects using Y"), when entities recur across documents, or when relationships ARE the data. If lookups are single-hop, use a table and stop.

2. Knowledge representation choice — Pick how facts are encoded: property graph (Neo4j-style, pragmatic default), RDF triples (interop/standards), or plain typed edges in JSON/SQLite (small scale). Decide now how time and provenance attach to every fact.

3. Ontology modeling — Define entity types, relation types (with domain/range), and attributes BEFORE extraction. Start minimal: 5-15 entity types, 10-30 relation types. Two rules from the course: every relation gets a precise verb name (ACQUIRED, not RELATED_TO), and if two types are always queried together, merge them. Details and worked examples: references/modeling.md

4. Entity extraction (NER) — Extract typed entities from sources. Method ladder: exact rules/dictionaries for closed vocabularies → LLM extraction with the ontology in the prompt for open text. Always extract with span + source pointer for provenance.

5. Relation extraction — Extract typed edges between recognized entities. Constrain the LLM to the ontology's relation list with domain/range checks; reject edges whose endpoints have incompatible types. This one validation step removes most hallucinated structure.

6. Event extraction — For dynamic domains (news, logs, transactions), extract events as first-class nodes (trigger + typed arguments + time), not just static edges. Extraction methods, prompt patterns, and failure modes for stages 4-6: references/extraction.md

7. Quality gate — Before fusion, sample and score: entity precision (are extracted entities real and correctly typed?), relation precision (does the source sentence actually assert the edge?). Fix the prompt/rules, not the output, then re-run. Target ≥90% precision on a 50-item sample before proceeding — recall improves with more passes; bad precision poisons the graph permanently.

8. Knowledge fusion — Merge duplicates within and across sources: same real-world entity, different surface forms ("SEU" = "Southeast University" = "东南大学"). Blocking + matching + merge policy. Skipping this is the #1 cause of useless graphs. Matching strategies: references/fusion-and-llm.md

9. Serve to LLMs (KG × LLM) — Make the graph useful to agents: GraphRAG retrieval (subgraph → context), graph-as-memory (agent writes facts back through stages 4-8), and LLM-as-reasoner over paths. Patterns and pitfalls: references/fusion-and-llm.md

Working Rules

• Schema first, always. Extraction without an ontology produces a "graph" that is really a word cloud with arrows. If the user resists schema design, build the minimal 5-type ontology from 3 sample documents and show it for approval.
• Provenance on every fact. Each node/edge stores source, extracted_at, and confidence. Non-negotiable — fusion (stage 8) and trust both depend on it.
• Incremental over big-bang. Process a 10-document pilot through all 9 stages before scaling. The pilot exposes ontology gaps at 1% of the cost.
• LLM extraction is stage machinery, not the pipeline. The LLM slots into stages 4-6; the surrounding schema, validation, and fusion are what make the output a knowledge graph.

Reference Files

• references/curriculum.md — Full translated curriculum of the source course with per-lecture summaries and links to the original Chinese slide decks. Read when the user wants theory depth, the academic grounding, or the original materials.
• references/modeling.md — Knowledge representation & ontology engineering (course lectures 2-3). Read during stages 2-3.
• references/extraction.md — Entity, relation, and event extraction from rules to LLM prompting (lectures 4-7). Read during stages 4-7.
• references/fusion-and-llm.md — Knowledge fusion and KG × LLM integration (lectures 8-9). Read during stages 8-9.

Credits

Distilled and translated from 东南大学《知识图谱》研究生课程 (Southeast University graduate course on Knowledge Graphs), Prof. Peng Wang — https://github.com/npubird/KnowledgeGraphCourse. All original lecture PDFs are in Chinese; this skill is an independent English distillation adapted for AI-agent workflows.