Dense Scores

as donto believed on

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-09.)

Dense Scores

Rdf:type	Array^[10]
Rdf:type	Input Parameter^[8]
Rdfs:label	dense_scores^[8]
Rdfs:label	dense scores^[1]
Computed From	Query List^[2]
Shape	(15000, 1000)^[5]
Generated by	np.random.rand^[5]
Has Value	[0.7, 0.3, 0.1]^[6]
Used in	Hybrid Ranking^[14]
Described As	Example dense scores^[4]

ex:dense_scores

Dense Scores has 28 facts recorded in Dontopedia across 14 references, with 2 live disagreements.

28 facts·14 predicates·14 sources·2 in dispute

Mostly:rdf:type(12), rdfs:label(4), computed from(1)

Rdf:typein dispute`rdf:type`

Array^[10]all time · 8099970e F2d8 437f 874b E1c72a22eeb0
Array^[11]all time · Ac759ab9 7ab3 4ec2 B6de 0d28a3f4e0cf
Input Parameter^[8]all time · A473407e 8449 4e78 89b6 989e8d589870
Numpy Array^[6]all time · Ce953854 D151 4cac B4e7 C4c5a5583796
Num Py Array^[12]all time · 6223a392 38d5 4eaa 966d Ea0055735550
Parameter^[13]all time · B5922a4d 0e9e 426c Bf72 B2561710a1f7
Score Array^[14]all time · Aecfc98e 8fd4 42fe 813a F35940e06f50
Scores^[2]all time · B2fa8237 A2ba 45f1 B609 1096fd02ce18
Score Type^[1]all time · 8419193f 8cac 4d94 919a B1c2084db6fd
Variable^[5]all time · Adfabb1c 3382 4bcc 93d2 Ae36f6f2c458

Rdfs:labelin dispute`rdfs:label`

dense_scores^[8]all time · A473407e 8449 4e78 89b6 989e8d589870
dense_scores^[9]sourceall time · B4174542 E9f5 41d0 809f Ec6511b667bb
dense scores^[1]all time · 8419193f 8cac 4d94 919a B1c2084db6fd
dense_scores^[3]all time · Ad9f402f Ddf2 4c49 9c7e E59f03a5935c

Computed From`computedFrom`

Query List^[2]sourceall time · B2fa8237 A2ba 45f1 B609 1096fd02ce18

Shape`shape`

(15000, 1000)^[5]all time · Adfabb1c 3382 4bcc 93d2 Ae36f6f2c458

Generated by`generatedBy`

np.random.rand^[5]all time · Adfabb1c 3382 4bcc 93d2 Ae36f6f2c458

Has Value`hasValue`

[0.7, 0.3, 0.1]^[6]sourceall time · Ce953854 D151 4cac B4e7 C4c5a5583796

Used in`usedIn`

Hybrid Ranking^[14]sourceall time · Aecfc98e 8fd4 42fe 813a F35940e06f50

Described As`describedAs`

Example dense scores^[4]sourceall time · F2ffcb18 D871 49d2 8d5c 2b469917574c

Value Source`valueSource`

np.random.rand^[4]sourceall time · F2ffcb18 D871 49d2 8d5c 2b469917574c

Size`size`

25000^[4]sourceall time · F2ffcb18 D871 49d2 8d5c 2b469917574c

Type`type`

numpy array^[4]all time · F2ffcb18 D871 49d2 8d5c 2b469917574c

Is Parameter of`isParameterOf`

Evaluate Relevance Lift Function^[7]sourceall time · E3d6146f 0be0 4107 8509 B0471fc829a9

Inbound mentions (17)

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.

`combines`Combines(4)

Combinationex:combination
Hybrid Ranking Algorithmex:hybrid_ranking_algorithm
Linear Combinationex:linear_combination
Rank Documentsex:rank_documents

`computedFrom`Computed From(3)

Hybrid Scoresex:hybrid_scores
Mismatchex:mismatch
Mismatchesex:mismatches

`hasParameter`Has Parameter(3)

Evaluate Relevance Liftex:evaluate_relevance_lift
Rank Documentsex:rank_documents
Log Score Mismatcheslog_score_mismatches

`derivedFrom`Derived From(2)

Dense Scores Normalizedex:dense_scores_normalized
Dense Scores Normalizedex:dense_scores_normalized

`combinedWith`Combined With(1)

Sparse Scoresex:sparse_scores

`compares`Compares(1)

Mismatchex:mismatch

`consistsOf`Consists of(1)

Dense Retrievalex:dense_retrieval

`definesVariable`Defines Variable(1)

Example Usageex:example-usage

`takesArgument`Takes Argument(1)

Hybrid Rankingex:hybrid_ranking

Other facts (2)

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.

2 facts

Predicate	Value	Ref
Derived From	Cosine Similarity	[3]
Combined With	Sparse Scores	[1]

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.

28 undated

—

combinedWithbeam/8419193f-8cac-4d94-919a-b1c2084db6fd

ex:sparse_scores

—

computedFrombeam/b2fa8237-a2ba-45f1-b609-1096fd02ce18

ex:query-list

—

derivedFrombeam/ad9f402f-ddf2-4c49-9c7e-e59f03a5935c

ex:cosine_similarity

—

describedAsbeam/f2ffcb18-d871-49d2-8d5c-2b469917574c

Example dense scores

—

generatedBybeam/adfabb1c-3382-4bcc-93d2-ae36f6f2c458

np.random.rand

—

hasValuebeam/ce953854-d151-4cac-b4e7-c4c5a5583796

[0.7, 0.3, 0.1]

—

isParameterOfbeam/e3d6146f-0be0-4107-8509-b0471fc829a9

ex:evaluate_relevance_lift_function

—

labelbeam/a473407e-8449-4e78-89b6-989e8d589870

dense_scores

—

labelbeam/b4174542-e9f5-41d0-809f-ec6511b667bb

dense_scores

—

labelbeam/8419193f-8cac-4d94-919a-b1c2084db6fd

dense scores

—

labelbeam/ad9f402f-ddf2-4c49-9c7e-e59f03a5935c

dense_scores

—

typebeam/8099970e-f2d8-437f-874b-e1c72a22eeb0

ex:Array

—

typebeam/ac759ab9-7ab3-4ec2-b6de-0d28a3f4e0cf

ex:Array

—

typebeam/a473407e-8449-4e78-89b6-989e8d589870

ex:InputParameter

—

typebeam/ce953854-d151-4cac-b4e7-c4c5a5583796

ex:NumpyArray

—

typebeam/6223a392-38d5-4eaa-966d-ea0055735550

ex:NumPyArray

—

typebeam/b5922a4d-0e9e-426c-bf72-b2561710a1f7

ex:Parameter

—

typebeam/aecfc98e-8fd4-42fe-813a-f35940e06f50

ex:ScoreArray

—

typebeam/b2fa8237-a2ba-45f1-b609-1096fd02ce18

ex:Scores

—

typebeam/8419193f-8cac-4d94-919a-b1c2084db6fd

ex:ScoreType

—

typebeam/adfabb1c-3382-4bcc-93d2-ae36f6f2c458

ex:Variable

—

typebeam/b4174542-e9f5-41d0-809f-ec6511b667bb

ex:Variable

—

typebeam/ad9f402f-ddf2-4c49-9c7e-e59f03a5935c

ex:Variable

—

shapebeam/adfabb1c-3382-4bcc-93d2-ae36f6f2c458

(15000, 1000)

—

sizebeam/f2ffcb18-d871-49d2-8d5c-2b469917574c

25000

—

typebeam/f2ffcb18-d871-49d2-8d5c-2b469917574c

numpy array

—

usedInbeam/aecfc98e-8fd4-42fe-813a-f35940e06f50

ex:hybrid_ranking

—

valueSourcebeam/f2ffcb18-d871-49d2-8d5c-2b469917574c

np.random.rand

References (14)

14 references

[1]beam-chunk3 facts
customctx:claims/beam/8419193f-8cac-4d94-919a-b1c2084db6fd
- full textbeam-chunk
  text/plain1 KBdoc:beam/8419193f-8cac-4d94-919a-b1c2084db6fd
  Show excerpt
  alphas = np.linspace(0, 1, 11) # Range of alpha values to test best_alpha, best_map = {}, {} for query in queries: best_alpha[query], best_map[query] = tune_alpha(query, documents, relevant_docs[query], alphas) print(f"Best alpha f…
[2]beam-chunk2 facts
customctx:claims/beam/b2fa8237-a2ba-45f1-b609-1096fd02ce18
- full textbeam-chunk
  text/plain1 KBdoc:beam/b2fa8237-a2ba-45f1-b609-1096fd02ce18
  Show excerpt
  vectorizer = TfidfVectorizer() tfidf_matrix = vectorizer.fit_transform(documents) query_vector = vectorizer.transform([query]) similarity_scores = (query_vector * tfidf_matrix.T).toarray() return similarity_scores def h…
[3]Ad9f402f Ddf2 4c49 9c7e E59f03a5935c3 facts
customctx:claims/beam/ad9f402f-ddf2-4c49-9c7e-e59f03a5935c
[4]beam-chunk4 facts
customctx:claims/beam/f2ffcb18-d871-49d2-8d5c-2b469917574c
- full textbeam-chunk
  text/plain1 KBdoc:beam/f2ffcb18-d871-49d2-8d5c-2b469917574c
  Show excerpt
  dense_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…
[5]Adfabb1c 3382 4bcc 93d2 Ae36f6f2c4583 facts
customctx:claims/beam/adfabb1c-3382-4bcc-93d2-ae36f6f2c458
[6]beam-chunk2 facts
customctx:claims/beam/ce953854-d151-4cac-b4e7-c4c5a5583796
- full textbeam-chunk
  text/plain1 KBdoc:beam/ce953854-d151-4cac-b4e7-c4c5a5583796
  Show excerpt
  # Calculate score mismatches mismatches = np.abs(sparse_scores - dense_scores) # Find indices where mismatches exceed the threshold mismatch_indices = np.where(mismatches > threshold)[0] # Log detailed informat…
[7]beam-chunk1 fact
customctx:claims/beam/e3d6146f-0be0-4107-8509-b0471fc829a9
- full textbeam-chunk
  text/plain896 Bdoc:beam/e3d6146f-0be0-4107-8509-b0471fc829a9
  Show excerpt
  precision = precision_at_k(true_labels, predicted_labels, k=k) if precision > best_precision: best_precision = precision best_alpha = alpha print(f"Best Alpha: {best_alpha}, Best Precision@{k…
[8]beam-chunk2 facts
customctx:claims/beam/a473407e-8449-4e78-89b6-989e8d589870
- full textbeam-chunk
  text/plain1 KBdoc:beam/a473407e-8449-4e78-89b6-989e8d589870
  Show excerpt
  query = request.json['query'] results = es.search(index="documents", body={"query": {"match": {"text": query}}}) return jsonify(results) if __name__ == '__main__': app.run(host='0.0.0.0', port=5000) ``` - **Den…
[9]beam-chunk2 facts
customctx:claims/beam/b4174542-e9f5-41d0-809f-ec6511b667bb
- full textbeam-chunk
  text/plain1 KBdoc:beam/b4174542-e9f5-41d0-809f-ec6511b667bb
  Show excerpt
  dense_scores = get_embeddings([query]).dot(embeddings.T) combined_scores = 0.5 * sparse_scores + 0.5 * dense_scores return combined_scores # Example usage documents = ["This is a sample document.", "Este es un documento de mues…
[10]beam-chunk1 fact
customctx:claims/beam/8099970e-f2d8-437f-874b-e1c72a22eeb0
- full textbeam-chunk
  text/plain1 KBdoc:beam/8099970e-f2d8-437f-874b-e1c72a22eeb0
  Show excerpt
  Assuming you have a function `rank_documents` that combines sparse and dense scores, here are some unit tests you can write using the `unittest` framework in Python: ```python import unittest import numpy as np def rank_documents(query, s…
[11]beam-chunk1 fact
customctx:claims/beam/ac759ab9-7ab3-4ec2-b6de-0d28a3f4e0cf
- full textbeam-chunk
  text/plain1 KBdoc:beam/ac759ab9-7ab3-4ec2-b6de-0d28a3f4e0cf
  Show excerpt
  mismatch_indices = np.where(mismatches > threshold)[0] # Log detailed information for each significant mismatch for idx in mismatch_indices: logging.warning( json.dumps({ 'query_id': quer…
[12]beam-chunk1 fact
customctx:claims/beam/6223a392-38d5-4eaa-966d-ea0055735550
- full textbeam-chunk
  text/plain1 KBdoc:beam/6223a392-38d5-4eaa-966d-ea0055735550
  Show excerpt
  # 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( …
[13]B5922a4d 0e9e 426c Bf72 B2561710a1f71 fact
customctx:claims/beam/b5922a4d-0e9e-426c-bf72-b2561710a1f7
[14]beam-chunk2 facts
customctx:claims/beam/aecfc98e-8fd4-42fe-813a-f35940e06f50
- full textbeam-chunk
  text/plain1 KBdoc:beam/aecfc98e-8fd4-42fe-813a-f35940e06f50
  Show excerpt
  expected_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, […

Keep researching

Missing something or suspicious of what's here? Kick off a research session — a Claude agent will investigate, cite its sources, and file new facts into a dedicated context you can review before accepting into the shared view.

Dense Scores

Rdf:typein disputerdf:type

Rdfs:labelin disputerdfs:label

Computed FromcomputedFrom

Shapeshape

Generated bygeneratedBy

Has ValuehasValue

Used inusedIn

Described AsdescribedAs

Value SourcevalueSource

Sizesize

Typetype

Is Parameter ofisParameterOf

Inbound mentions (17)

combinesCombines(4)

computedFromComputed From(3)

hasParameterHas Parameter(3)

derivedFromDerived From(2)

combinedWithCombined With(1)

comparesCompares(1)

consistsOfConsists of(1)

definesVariableDefines Variable(1)

takesArgumentTakes Argument(1)

Other facts (2)

Timeline

References (14)

See also

Keep researching

Rdf:typein dispute`rdf:type`

Rdfs:labelin dispute`rdfs:label`

Computed From`computedFrom`

Shape`shape`

Generated by`generatedBy`

Has Value`hasValue`

Used in`usedIn`

Described As`describedAs`

Value Source`valueSource`

Size`size`

Type`type`

Is Parameter of`isParameterOf`

`combines`Combines(4)

`computedFrom`Computed From(3)

`hasParameter`Has Parameter(3)

`derivedFrom`Derived From(2)

`combinedWith`Combined With(1)

`compares`Compares(1)

`consistsOf`Consists of(1)

`definesVariable`Defines Variable(1)

`takesArgument`Takes Argument(1)