Phase 4: NLP Applications · ~60 minutes · Python

Relation Extraction & Knowledge Graph Construction

NER found the entities. Entity linking anchored them. Relation extraction finds the edges between them. A knowledge graph is the sum of nodes, edges, and their provenance.

Hiring signal: Understanding of relation extraction & knowledge graph construction internals

What you will learn

Implement relation extraction & knowledge graph construction from scratch
Understand the math and intuition behind the algorithm
Use production libraries for the same task
Ship a reusable artifact

Introduction

Type: Build Languages: Python Prerequisites: Phase 5 · 06 (NER), Phase 5 · 25 (Entity Linking) Time: ~60 minutes

The Problem

An analyst reads: "Tim Cook became CEO of Apple in 2011." Four facts:

(Tim Cook, role, CEO)
(Tim Cook, employer, Apple)
(Tim Cook, start_date, 2011)
(Apple, type, Organization)

Relation Extraction (RE) turns free text into structured triples (subject, relation, object). Aggregate across a corpus and you have a knowledge graph. Aggregate and query and you have a reasoning substrate for RAG, analytics, or compliance audits.

The 2026 problem: LLMs extract relations enthusiastically. Too enthusiastically. They hallucinate triples that the source text does not support. Without provenance, you cannot tell real triples from plausible fiction. The 2026 answer is AEVS-style anchor-and-verify pipelines.

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Concept, Build It, Pitfalls, Use It, Ship It, Exercises, Key Terms, Further Reading — plus a hands-on lab, quiz, and project artifact.

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