Dontopedia

get_synonyms

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)

get_synonyms has 41 facts recorded in Dontopedia across 5 references, with 6 live disagreements.

41 facts·28 predicates·5 sources·6 in dispute

Mostly:rdf:type(5), has parameter(3), returns(3)

Maturity scale raw canonical shape-checked rule-derived certified

Inbound mentions (13)

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.

usedByUsed by(4)

demonstratesDemonstrates(2)

assignedByAssigned by(1)

assignedFromAssigned From(1)

consistsOfConsists of(1)

createdByCreated by(1)

definesFunctionDefines Function(1)

implementedByImplemented by(1)

relatesRelates(1)

Other facts (40)

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.

40 facts
PredicateValueRef
Rdf:typePython Function[1]
Rdf:typeFunction Call[2]
Rdf:typeFunction[3]
Rdf:typeFunction[4]
Rdf:typePython Function[5]
Has Parameterterm[2]
Has Parameterterm[3]
Has Parameterword[5]
ReturnsSynonyms Variable[2]
ReturnsClosest Synonyms[3]
ReturnsList of Synonyms[5]
Calls Methodwordnet.synsets[5]
Calls Methodsyn.lemmas[5]
Calls Methodlemma.name[5]
PurposeFind synonyms using WordNet[4]
Purposeretrieve-synonyms-for-word[5]
Iteration Variablesyn[5]
Iteration Variablelemma[5]
Has Parameterterm[1]
CallsGet Contextual Embeddings Function[1]
ComputesSimilarity Scores[1]
Is Defined inExternal Library[2]
ImplementsSynonym Expansion Task[3]
Function Nameget_synonyms[4]
Parameterword[4]
Return Typelist[4]
Contains LoopWordnet Synsets Loop[4]
UsesWordnet[4]
Uses Data Structureset[5]
Initializes Variablesynonyms[5]
Data Structure Typeset[5]
Nested LoopLemmas Iteration[5]
Outer LoopSynsets Iteration[5]
Adds to Collectionlemma.name[5]
Converts tolist[5]
Algorithmsynset-then-lemma-iteration[5]
Returns Typelist[5]
Uses Nested Loopstrue[5]
Outer Loop Targetwordnet.synsets[5]
Inner Loop Targetsyn.lemmas[5]

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/5d8a681b-1fe3-4aff-8534-8603ba9d9bfc
ex:Python-Function
has-parameterbeam/5d8a681b-1fe3-4aff-8534-8603ba9d9bfc
term
callsbeam/5d8a681b-1fe3-4aff-8534-8603ba9d9bfc
ex:get-contextual-embeddings-function
computesbeam/5d8a681b-1fe3-4aff-8534-8603ba9d9bfc
ex:similarity-scores
typebeam/a296a949-2c13-4366-96e2-0759ac1499ba
ex:FunctionCall
isDefinedInbeam/a296a949-2c13-4366-96e2-0759ac1499ba
ex:external-library
hasParameterbeam/a296a949-2c13-4366-96e2-0759ac1499ba
term
returnsbeam/a296a949-2c13-4366-96e2-0759ac1499ba
ex:synonyms-variable
typebeam/5e1fccc0-109f-4d58-b6c4-6482a168aad7
ex:Function
labelbeam/5e1fccc0-109f-4d58-b6c4-6482a168aad7
get_synonyms
hasParameterbeam/5e1fccc0-109f-4d58-b6c4-6482a168aad7
term
returnsbeam/5e1fccc0-109f-4d58-b6c4-6482a168aad7
ex:closest-synonyms
implementsbeam/5e1fccc0-109f-4d58-b6c4-6482a168aad7
ex:synonym-expansion-task
typebeam/03e9535f-b129-47f6-9c40-934a5df3e95a
ex:Function
functionNamebeam/03e9535f-b129-47f6-9c40-934a5df3e95a
get_synonyms
parameterbeam/03e9535f-b129-47f6-9c40-934a5df3e95a
word
returnTypebeam/03e9535f-b129-47f6-9c40-934a5df3e95a
list
purposebeam/03e9535f-b129-47f6-9c40-934a5df3e95a
Find synonyms using WordNet
containsLoopbeam/03e9535f-b129-47f6-9c40-934a5df3e95a
ex:wordnet-synsets-loop
usesbeam/03e9535f-b129-47f6-9c40-934a5df3e95a
ex:wordnet
typebeam/524c612c-d2c8-4637-96e1-a8bf9b0b6122
ex:PythonFunction
hasParameterbeam/524c612c-d2c8-4637-96e1-a8bf9b0b6122
word
returnsbeam/524c612c-d2c8-4637-96e1-a8bf9b0b6122
ex:list-of-synonyms
usesDataStructurebeam/524c612c-d2c8-4637-96e1-a8bf9b0b6122
set
iterationVariablebeam/524c612c-d2c8-4637-96e1-a8bf9b0b6122
syn
iterationVariablebeam/524c612c-d2c8-4637-96e1-a8bf9b0b6122
lemma
callsMethodbeam/524c612c-d2c8-4637-96e1-a8bf9b0b6122
wordnet.synsets
callsMethodbeam/524c612c-d2c8-4637-96e1-a8bf9b0b6122
syn.lemmas
callsMethodbeam/524c612c-d2c8-4637-96e1-a8bf9b0b6122
lemma.name
purposebeam/524c612c-d2c8-4637-96e1-a8bf9b0b6122
retrieve-synonyms-for-word
initializesVariablebeam/524c612c-d2c8-4637-96e1-a8bf9b0b6122
synonyms
dataStructureTypebeam/524c612c-d2c8-4637-96e1-a8bf9b0b6122
set
nestedLoopbeam/524c612c-d2c8-4637-96e1-a8bf9b0b6122
ex:lemmas-iteration
outerLoopbeam/524c612c-d2c8-4637-96e1-a8bf9b0b6122
ex:synsets-iteration
addsToCollectionbeam/524c612c-d2c8-4637-96e1-a8bf9b0b6122
lemma.name
convertsTobeam/524c612c-d2c8-4637-96e1-a8bf9b0b6122
list
algorithmbeam/524c612c-d2c8-4637-96e1-a8bf9b0b6122
synset-then-lemma-iteration
returnsTypebeam/524c612c-d2c8-4637-96e1-a8bf9b0b6122
list
usesNestedLoopsbeam/524c612c-d2c8-4637-96e1-a8bf9b0b6122
true
outerLoopTargetbeam/524c612c-d2c8-4637-96e1-a8bf9b0b6122
wordnet.synsets
innerLoopTargetbeam/524c612c-d2c8-4637-96e1-a8bf9b0b6122
syn.lemmas

References (5)

5 references
  1. ctx:claims/beam/5d8a681b-1fe3-4aff-8534-8603ba9d9bfc
    • full textbeam-chunk
      text/plain1 KBdoc:beam/5d8a681b-1fe3-4aff-8534-8603ba9d9bfc
      Show excerpt
      inputs = tokenizer(term, return_tensors='pt', padding=True, truncation=True) outputs = model(**inputs) embeddings = outputs.last_hidden_state.mean(dim=1) # Mean pooling return embeddings ``` ### Step 4: Retrieve Synonyms B
  2. ctx:claims/beam/a296a949-2c13-4366-96e2-0759ac1499ba
    • full textbeam-chunk
      text/plain995 Bdoc:beam/a296a949-2c13-4366-96e2-0759ac1499ba
      Show excerpt
      return closest_synonyms # Test the synonym expansion terms = ["happy", "sad", "angry"] for term in terms: synonyms = get_synonyms(term) print(f"Synonyms for '{term}': {synonyms}") ``` ### Summary 1. **Setup Environment**: Ens
  3. ctx:claims/beam/5e1fccc0-109f-4d58-b6c4-6482a168aad7
    • full textbeam-chunk
      text/plain1 KBdoc:beam/5e1fccc0-109f-4d58-b6c4-6482a168aad7
      Show excerpt
      for word, synonyms in thesaurus.items(): word_embedding = get_contextual_embeddings(word) similarities = [np.dot(term_embedding, get_contextual_embeddings(syn)) for syn in synonyms] closest_synonyms.extend([synon
  4. ctx:claims/beam/03e9535f-b129-47f6-9c40-934a5df3e95a
    • full textbeam-chunk
      text/plain1 KBdoc:beam/03e9535f-b129-47f6-9c40-934a5df3e95a
      Show excerpt
      Here's an example of a hybrid approach that combines WordNet and context-aware embeddings: ```python from transformers import BertTokenizer, BertModel import torch import nltk from nltk.corpus import wordnet nltk.download('wordnet') toke
  5. ctx:claims/beam/524c612c-d2c8-4637-96e1-a8bf9b0b6122
    • full textbeam-chunk
      text/plain1 KBdoc:beam/524c612c-d2c8-4637-96e1-a8bf9b0b6122
      Show excerpt
      - **Dataset Characteristics**: If your dataset has specific characteristics or domain-specific language, you might want to experiment with both models to see which performs better on your particular data. ### Conclusion For query reformula

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