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False Positives has 10 facts recorded in Dontopedia across 3 references, with 1 live disagreement.
Mostly:rdf:type(3), condition for(1), used in(1)
definitionrdfs:labelinputTocalculatedBycalculationMethodOther 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.
dependsOnDepends on(1)ex:calculate_precision_and_recallincludesIncludes(1)ex:confusion_matrixinvolvesCalculationInvolves Calculation(1)ex:Step_2sourceOfSource of(1)ex:retrieved_neighborsTimeline 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.
doc:beam/6dbe8f35-74b9-40c2-9797-0debc6fb19f9true_positives = sum([1 for vec in retrieved_neighbors if vec in true_neighbors]) false_positives = len(retrieved_neighbors) - true_positives false_negatives = len(true_neighbors) - true_positives recall_rate = true_positive…
doc:beam/cbbe7ac5-f47d-4434-83e6-aafcb6d39ebdprecision_values = [] recall_values = [] for _ in range(num_trials): precision, recall = calculate_precision_and_recall(threshold, test_terms) precision_values.append(precision) recall_values.append(recal…
doc:beam/25c4eff6-2519-424f-918f-6c27fab25d72# Simulate the probability of correct synonym expansion return np.random.rand() < threshold def calculate_precision_and_recall(threshold, test_terms): true_positives = 0 false_positives = 0 false_negatives = 0 …
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