opensanctions / opensanctions/poliloom

Report on rejected positions to identify vague/generic positions

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loom
Dominant language
Python
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22
Forks
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Description

Building on feedback from @wolfgang8741: use the rejection data we already capture to surface vague/generic positions and the classes they share.

Context

Rejections are already stored — rejecting a property appends an Evaluation(is_accepted = false) linked to the Property (which references the position QID via entity_id). No new capture mechanism is needed.

Task

Build/run reporting against the prod DB to surface:

  • Most frequently rejected positions (with rejection rate, not just raw counts, and a min-volume guard)
  • Shared classes in the P279 subclass tree those rejected positions funnel up to

The interesting output is the mid-level ancestor classes with high coverage — those are candidates to review and add to Position._hierarchy_ignore (which already excludes things like occupation, historical position, award).

Reference SPARQL from the discussion: ~35% of the 242k position-like items in Wikidata lack a P17 country — possibly a separate follow-up.

cc @wolfgang8741

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First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start by tracing the existing Evaluation and Property records that store rejected positions, then inspect Position._hierarchy_ignore and the available production database reporting or query entry points. Done means reporting the most-rejected positions with rejection rates and a minimum-volume guard, plus shared mid-level P279 ancestors with high coverage.

Written by the indexing model from the issue text.

Assessment

Tech stack
python
Domain
data, database
Issue type
Feature
Difficulty
4/5
Estimated time
3-5 days
Activity status
Quiet
Clarity
Mostly clear
Newbie friendliness
42/100

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