Dataset Structure
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)
Dataset Structure has 4 facts recorded in Dontopedia across 3 references, with 1 live disagreement.
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (2)
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.
concernsConcerns(1)
- User Uncertainty
ex:user-uncertainty
isDesignedForIs Designed for(1)
- Metrics Calculation Function
ex:metrics-calculation-function
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 |
|---|---|---|
| Has Field | Query Field | [2] |
| Has Field | Label Field | [2] |
| Is Suitable for | Statistical Analysis | [1] |
| Affects | Evaluation Quality | [3] |
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.
References (3)
ctx:claims/beam/cfcb4b3f-8f03-488b-a124-22fc69ac8282- full textbeam-chunktext/plain1 KB
doc:beam/cfcb4b3f-8f03-488b-a124-22fc69ac8282Show excerpt
- The `apply` method is used with `axis=1` to apply the function row-wise, which is efficient for pandas DataFrames. - The `correction_rules` function is optimized to handle edge cases and return `None` if an error occurs. 4. **Docst…
ctx:claims/beam/48adae40-4bfc-4307-b82a-a3732c282daf- full textbeam-chunktext/plain1 KB
doc:beam/48adae40-4bfc-4307-b82a-a3732c282dafShow excerpt
Would you like to proceed with these steps or do you have any specific questions about any part of the process? [Turn 10576] User: Sure, let's start by experimenting with NLTK and spaCy to see which one works better for my spelling correct…
ctx:claims/beam/ca2653b8-c25f-4a54-bdfa-ff6ea71f5472- full textbeam-chunktext/plain1 KB
doc:beam/ca2653b8-c25f-4a54-bdfa-ff6ea71f5472Show excerpt
true_vector = [doc in ground_truth_documents for doc in retrieved_documents] pred_vector = [True] * len(retrieved_documents) y_true.extend(true_vector) y_pred.extend(pred_vector) # Calculate precision and recall precision …
See also
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