Knowledge Graph Construction

What It Is

The process of creating structured representations of knowledge as entities, relationships, and attributes organized in a graph structure. Nodes represent things; edges represent relationships between things.

Construction Approaches

Manual/Expert-Driven: Domain experts define schema and populate facts. High quality, expensive, doesn’t scale.

Rule-Based Extraction: Pattern matching and NLP rules extract triples from text. Scalable but brittle.

Statistical/ML Extraction: Models learn to identify entities and relations. More robust but introduces errors.

LLM-Assisted: Large language models extract structured knowledge from unstructured text. Powerful but requires validation (LLMs may hallucinate relationships.

The Quality Tradeoffs

Coverage vs. Precision: Broader extraction catches more, but with more errors. Schema Rigidity vs. Flexibility: Strict schemas ensure consistency but miss edge cases. Automation vs. Expert Input: Fully automated is cheap but lower quality.

The Paradigm Inversion

Historically, knowledge graphs were the goal (LLMs helped build them. Increasingly, knowledge graphs are infrastructure, providing grounding for LLM reasoning. This changes what “quality” means: coverage for retrieval may matter more than logical completeness.

Related: 07-molecule—vectors-vs-graphs, 07-organism—when-knowledge-graphs-become-memory, 05-molecule—rag-architecture-taxonomy