Dontopedia

NDCG

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)

NDCG has 9 facts recorded in Dontopedia across 3 references, with 1 live disagreement.

9 facts·7 predicates·3 sources·1 in dispute

Mostly:rdf:type(2), full form(1), described as(1)

Maturity scale raw canonical shape-checked rule-derived certified

Inbound mentions (6)

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.

containsContains(1)

includesIncludes(1)

includesMetricsIncludes Metrics(1)

returnsReturns(1)

returnsMultipleValuesReturns Multiple Values(1)

usedInUsed in(1)

Other facts (8)

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.

8 facts
PredicateValueRef
Rdf:typeMetric[1]
Rdf:typeVariable[3]
Full FormNormalized Discounted Cumulative Gain[1]
Described Asmeasures the usefulness of the top-k retrieved documents[1]
Abbreviation forNormalized Discounted Cumulative Gain[1]
Returned byCalculate Metrics[2]
Computed byCalculate Metrics[2]
Assigned byCalculate Metrics[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.

typebeam/23c0eddb-0929-4239-8d55-13531af3e8f5
ex:Metric
labelbeam/23c0eddb-0929-4239-8d55-13531af3e8f5
NDCG
fullFormbeam/23c0eddb-0929-4239-8d55-13531af3e8f5
Normalized Discounted Cumulative Gain
describedAsbeam/23c0eddb-0929-4239-8d55-13531af3e8f5
measures the usefulness of the top-k retrieved documents
abbreviationForbeam/23c0eddb-0929-4239-8d55-13531af3e8f5
Normalized Discounted Cumulative Gain
returnedBybeam/f815a6d5-3a79-40fc-bcfc-c90172294821
ex:calculate_metrics
computedBybeam/f815a6d5-3a79-40fc-bcfc-c90172294821
ex:calculate_metrics
typebeam/120de523-8aa9-44e6-a94f-a9f5d853f0a8
ex:Variable
assignedBybeam/120de523-8aa9-44e6-a94f-a9f5d853f0a8
ex:calculate-metrics

References (3)

3 references
  1. ctx:claims/beam/23c0eddb-0929-4239-8d55-13531af3e8f5
    • full textbeam-chunk
      text/plain1 KBdoc:beam/23c0eddb-0929-4239-8d55-13531af3e8f5
      Show excerpt
      - **Average Precision (AP)**: Measure of precision at each relevant document. 4. **Mean Scores**: Calculate the mean of each metric across all queries. ### Additional Metrics 1. **Precision@k**: Precision of the top-k retrieved documen
  2. ctx:claims/beam/f815a6d5-3a79-40fc-bcfc-c90172294821
  3. ctx:claims/beam/120de523-8aa9-44e6-a94f-a9f5d853f0a8
    • full textbeam-chunk
      text/plain1 KBdoc:beam/120de523-8aa9-44e6-a94f-a9f5d853f0a8
      Show excerpt
      Here's how you can implement the calculation and visualization: ```python import numpy as np import matplotlib.pyplot as plt from sklearn.metrics import ndcg_score, average_precision_score def calculate_metrics(predictions, labels, k_ndcg

See also

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