Relevant Labels
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-05.)
Relevant Labels is Binary labels indicating whether a document is relevant (1) or not (0).
Mostly:rdf:type(2), first n set to one(1), description(1)
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.
definesVariableDefines Variable(1)
- Example Implementation
ex:example-implementation
parameterParameter(1)
- Retrieve Documents
ex:retrieve-documents
Other facts (5)
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.
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 (2)
ctx:claims/beam/18537b2d-1de5-488d-90f1-3d6d6503ecc3- full textbeam-chunktext/plain1 KB
doc:beam/18537b2d-1de5-488d-90f1-3d6d6503ecc3Show excerpt
1. **Generate Documents and Relevant Labels**: Create synthetic documents and labels indicating which documents are relevant. 2. **Implement Retrieval Tools**: Define how each retrieval tool works. For simplicity, let's assume each tool ret…
ctx:claims/beam/eb7f55ff-6715-4dd8-81f8-023b5f9693f2- full textbeam-chunktext/plain1 KB
doc:beam/eb7f55ff-6715-4dd8-81f8-023b5f9693f2Show excerpt
retrieved_labels = relevant_labels[retrieved_indices] true_positives = np.sum(retrieved_labels) recall = true_positives / num_relevant return recall # Initialize the recall scores recall_scores = [] for tool in tools: …
See also
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