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.
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (3)
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.
dependencyDependency(1)
- Padding and Truncation
ex:padding-and-truncation
dependsOnDepends on(1)
- Model and Context Handling
ex:model-and-context-handling
influenced-byInfluenced by(1)
- Model Choice
ex:model-choice
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.
| Predicate | Value | Ref |
|---|---|---|
| May Include | [2] | |
| May Include | Domain Specific Language | [2] |
| Includes | Sparse Data | [1] |
| Rdf:type | [2] |
Timeline
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References (2)
ctx:claims/beam/7835e578-f2e3-46a0-aa40-4497812bf8de- full textbeam-chunktext/plain1 KB
doc:beam/7835e578-f2e3-46a0-aa40-4497812bf8deShow 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…
ctx:claims/beam/63f3f6ff-b059-492e-954d-ccca67c2349d- full textbeam-chunktext/plain1020 B
doc:beam/63f3f6ff-b059-492e-954d-ccca67c2349dShow 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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