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Hybrid Scores has 17 facts recorded in Dontopedia across 4 references, with 3 live disagreements.
Mostly:computed from(5), result of(2), rdf:type(2)
rdf:typecomputedBystoredInassignedFromresultTypecomputedInusedByOther 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.
accessesArrayAccesses Array(1)ex:hybrid_scores_iappliedToApplied to(1)ex:array_indexingcomparesCompares(1)ex:mismatch_loggingcomputesVariableComputes Variable(1)ex:evaluate_relevance_liftreturnsReturns(1)ex:hybrid_rankingTimeline 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/6223a392-38d5-4eaa-966d-ea0055735550# Find indices where mismatches exceed the threshold mismatch_indices = np.where(mismatches > threshold)[0] # Log detailed information for each significant mismatch for idx in mismatch_indices: logger.warning( …
doc:beam/aecfc98e-8fd4-42fe-813a-f35940e06f50expected_scores = np.random.rand(25000) # Example expected scores # Compute hybrid scores hybrid_scores = hybrid_ranking(sparse_scores, dense_scores, alpha=0.6) # Log mismatches for i in range(len(expected_scores)): log_mismatch(i, […
doc:beam/f2ffcb18-d871-49d2-8d5c-2b469917574cdense_scores_normalized = normalize_scores(dense_scores) # Calculate weighted sum of sparse and dense scores hybrid_scores = alpha * sparse_scores_normalized + (1 - alpha) * dense_scores_normalized return hybrid_sc…
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