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AI Engineering/Phase 05/Lesson 25

Entity Linking & Disambiguation

NER found "Paris." Entity linking decides: Paris, France? Paris Hilton? Paris, Texas? Paris (the Trojan prince)? Without linking, your knowledge graph stays ambiguous.

Build 1 hr Python

Prerequisites: Phase 5 · 06 (NER), Phase 5 · 24 (Coreference Resolution)

#The Problem

A sentence reads: "Jordan beat the press." Your NER tags "Jordan" as PERSON. Good. But which Jordan?

  • Michael Jordan (basketball)?
  • Michael B. Jordan (actor)?
  • Michael I. Jordan (Berkeley ML professor — yes, this confusion is real in ML papers)?
  • Jordan (the country)?
  • Jordan (Hebrew first name)?

Entity linking (EL) resolves each mention to a unique entry in a knowledge base: Wikidata, Wikipedia, DBpedia, or your domain KB. Two subtasks:

  1. Candidate generation. Given "Jordan," which KB entries are plausible?
  2. Disambiguation. Given the context, which candidate is the right one?

Both steps are learnable. Both are benchmarked. The combined pipeline has been stable for a decade — what changes is the quality of the disambiguator.

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The text and code of this lesson are based on AI Engineering from Scratch by Rohit Ghumare, MIT licensed.