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entity-linking

entity-linking

Routes scispaCy candidate generation, knowledge-base loading, ANN index creation, and entity-linking workflows.

Independent GitHub snapshot · Automated classification · Original documentation governs

Skill 문서

GitHub

name: "entity-linking" description: "Routes scispaCy candidate generation, knowledge-base loading, ANN index creation, and entity-linking workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Entity Linking

Use this sub-skill when the task is about scispaCy's knowledge-base and entity-linking stack rather than the tokenizer or project-data workflows.

Typical triggers

  • Build or debug scispacy_linker.
  • Build an ANN index for a custom knowledge base.
  • Load UMLS, MeSH, GO, HPO, or RxNorm knowledge bases.
  • Inspect CandidateGenerator results or linker thresholds.
  • Work with KnowledgeBase, UmlsKnowledgeBase, or UMLS semantic type trees.
  • Handle cached_path downloads or linker cache issues.

What belongs here

  • Candidate generation and ANN index construction.
  • Built-in and custom knowledge-base loading.
  • UMLS semantic type tree helpers.
  • Linker factory configuration and abbreviation-aware linking.
  • Cache and remote-artifact behavior for KB/index downloads.

What does not belong here

  • Tokenization, sentence segmentation, abbreviation extraction, or hyponym detection. Use pipeline-components.
  • MedMentions/BIO readers, NER evaluation, and package/workflow scripts. Use project-workflows.
  • Package installation and model catalog details. Read the root installation reference.

First things to read or run

  • references/api-reference.md for verified signatures, defaults, and output shapes.
  • references/workflows.md for building custom linkers and querying candidates.
  • references/troubleshooting.md for cache, threshold, and nmslib issues.
  • ../../scripts/smoke_scispacy.py for the tiny linker smoke used to validate the environment.
  • scripts/build_linker_index.py when you want a bundled helper to build a linker index from a local KB.

Fast workflow summary

  1. Decide whether you need a built-in KB (umls, mesh, go, hpo, or rxnorm) or a custom KB file.
  2. Load or construct the KB.
  3. Build a candidate index with create_tfidf_ann_index or a bundled wrapper.
  4. Create the linker with EntityLinker or EntityLinker.from_kb.
  5. If you want abbreviation-aware linking, add the abbreviation detector first.

Example route decisions

  • If the request says "link biomedical mentions to UMLS", stay here.
  • If the request says "build an index from a JSONL knowledge base", stay here.
  • If the request says "count token frequencies from corpus text", switch to project-workflows.
  • If the request says "change how text is tokenized", switch to pipeline-components.

Verification mindset

For this sub-skill, the strongest validation is a tiny linker smoke:

  • KnowledgeBase can load a tiny local KB.
  • create_tfidf_ann_index(None, kb) can build a candidate index.
  • CandidateGenerator returns at least one candidate for a simple mention.
  • scispacy_linker can resolve abbreviations when the abbreviation detector is already in the pipeline.

If that smoke fails, read the troubleshooting reference before attempting a large KB build or a remote download.

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