Dontopedia

Dataset Characteristics

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)

Dataset Characteristics has 4 facts recorded in Dontopedia across 2 references, with 1 live disagreement.

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

Inbound mentions (3)

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dependencyDependency(1)

dependsOnDepends on(1)

influenced-byInfluenced by(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
May Include[2]
May IncludeDomain Specific Language[2]
IncludesSparse Data[1]
Rdf:type[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/7835e578-f2e3-46a0-aa40-4497812bf8de
ex:sparse-data
typebeam/63f3f6ff-b059-492e-954d-ccca67c2349d
ex:
mayIncludebeam/63f3f6ff-b059-492e-954d-ccca67c2349d
ex:
mayIncludebeam/63f3f6ff-b059-492e-954d-ccca67c2349d
ex:domain-specific-language

References (2)

2 references
  1. ctx:claims/beam/7835e578-f2e3-46a0-aa40-4497812bf8de
    • full textbeam-chunk
      text/plain1 KBdoc:beam/7835e578-f2e3-46a0-aa40-4497812bf8de
      Show excerpt
      recall = recall_score(y_test, predictions) print(f'{name} Recall score: {recall:.3f}') print(classification_report(y_test, predictions)) print(confusion_matrix(y_test, predictions)) print('-' * 50) ``` ### Explanat
  2. ctx:claims/beam/63f3f6ff-b059-492e-954d-ccca67c2349d
    • full textbeam-chunk
      text/plain1020 Bdoc:beam/63f3f6ff-b059-492e-954d-ccca67c2349d
      Show excerpt
      However, I'm only achieving about 80% accuracy with this approach. I've studied LLM-based reformulation and noted a 25% intent accuracy boost for 6,000 complex queries. Can you help me improve my implementation to reach at least 92% detecti

See also

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