Dontopedia

Uniform Interface

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-09.)

Uniform Interface has 3 facts recorded in Dontopedia across 2 references, with 1 live disagreement.

3 facts·2 predicates·2 sources·1 in dispute
Maturity scale raw canonical shape-checked rule-derived certified

Inbound mentions (6)

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.

adheresToAdheres to(1)

demonstrateDemonstrate(1)

demonstratesDemonstrates(1)

describesDescribes(1)

explainsExplains(1)

includesPrincipleIncludes Principle(1)

Other facts (3)

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.

3 facts
PredicateValueRef
IncludesStandard Http Methods[1]
IncludesConsistent Resource Naming[1]
Rdf:typeRest Principle[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.

includesbeam/fdf8898b-efa0-4bd1-8940-8157d32e6ff0
ex:standard-http-methods
includesbeam/fdf8898b-efa0-4bd1-8940-8157d32e6ff0
ex:consistent-resource-naming
typebeam/a8f42853-2865-4e3c-a260-ec8d3de4712d
ex:RESTPrinciple

References (2)

2 references
  1. ctx:claims/beam/fdf8898b-efa0-4bd1-8940-8157d32e6ff0
    • full textbeam-chunk
      text/plain1 KBdoc:beam/fdf8898b-efa0-4bd1-8940-8157d32e6ff0
      Show excerpt
      # For demonstration, let's assume we have a function `perform_vector_search` results = perform_vector_search(query_vector, top_k) return jsonify(results) api.add_resource(VectorSearch, '/vector-search') ```
  2. ctx:claims/beam/a8f42853-2865-4e3c-a260-ec8d3de4712d
    • full textbeam-chunk
      text/plain935 Bdoc:beam/a8f42853-2865-4e3c-a260-ec8d3de4712d
      Show excerpt
      # Perform vector search logic here results = perform_vector_search(query_vector, top_k) return jsonify(results) def post(self): data = request.get_json() query_vector = data.

See also

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