Dontopedia

Expansion Data

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)

Expansion Data has 4 facts recorded in Dontopedia across 2 references.

4 facts·4 predicates·2 sources

Mostly:rdf:type(1), has total size(1), is constrained by(1)

Maturity scale raw canonical shape-checked rule-derived certified

Inbound mentions (5)

Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.

appliesToApplies to(2)

isPartOfIs Part of(1)

operatesOnOperates on(1)

referencesReferences(1)

Other facts (4)

The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.

4 facts
PredicateValueRef
Rdf:typeDataset[1]
Has Total SizeFull Dataset[1]
Is Constrained by3 Percent Limit[1]
Has Exposure Limit3%[2]

Timeline

Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.

typebeam/7646fe36-4a34-4e09-b5b8-b96aa46b4805
ex:Dataset
hasTotalSizebeam/7646fe36-4a34-4e09-b5b8-b96aa46b4805
ex:full-dataset
isConstrainedBybeam/7646fe36-4a34-4e09-b5b8-b96aa46b4805
ex:3-percent-limit
hasExposureLimitbeam/b0c69968-148d-412a-8238-e75eb88b5ed2
3%

References (2)

2 references
  1. ctx:claims/beam/7646fe36-4a34-4e09-b5b8-b96aa46b4805
    • full textbeam-chunk
      text/plain1 KBdoc:beam/7646fe36-4a34-4e09-b5b8-b96aa46b4805
      Show excerpt
      password="password", realm_name="my-realm" ) # Get the realm realm = keycloak_admin.realm_name # Create a new role role = keycloak_admin.create_role( realm, "expanded-data-access", ["view", "edit"] ) # Limit exposure
  2. ctx:claims/beam/b0c69968-148d-412a-8238-e75eb88b5ed2
    • full textbeam-chunk
      text/plain1 KBdoc:beam/b0c69968-148d-412a-8238-e75eb88b5ed2
      Show excerpt
      print(f"Time to index 1000 documents: {end_time - start_time:.2f} seconds") # Run queries start_time = time.time() for doc in test_data: response = es.search(index='synonyms', body={ 'query': { 'match': {

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