Dontopedia

lemma

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

lemma has 23 facts recorded in Dontopedia across 11 references, with 2 live disagreements.

23 facts·10 predicates·11 sources·2 in dispute

Mostly:rdf:type(10), has attribute(3), has method(2)

Maturity scale raw canonical shape-checked rule-derived certified

Rdf:typein disputerdf:type

  • Attribute[1]sourceall time · 9e885203 13b0 4f18 89db 79cab2460230
  • Object[2]all time · 30196b02 E710 4de9 807e B72cfda7e001
  • Lemma[3]sourceall time · 82dc87bd 74b8 4fb6 Be5d 469ed934c86c
  • Linguistic Unit[4]all time · 6f825f15 5c97 4244 84f2 E40ee078d6ae
  • Variable[5]all time · 4be5ccbb C1b7 4c71 B494 78fd7c33ee6f
  • Lemma Object[6]all time · B27efc86 7008 4384 852a 049d06d255cb
  • Linguistic Unit[8]sourceall time · 869acbd5 0cda 40b0 94b3 06d5699021f2
  • Lemma[9]all time · 57e2ea52 F5cb 4239 Bf9f 3147a3b2efbc
  • Object[10]sourceall time · Eba347b2 A24e 4b7a Ab9b F7cd8535ecce
  • Lemma[11]all time · Edca9501 Cce9 465a 87b1 Ca97ba8c21a7

Inbound mentions (5)

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.

hasAttributeHas Attribute(1)

hasLemmaHas Lemma(1)

iteratesOverIterates Over(1)

iterationVariableIteration Variable(1)

loopsOverLoops Over(1)

Other facts (12)

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.

12 facts
PredicateValueRef
Has AttributeName[6]
Has Attributename[7]
Has AttributeName[11]
Has MethodName[2]
Has MethodName[10]
Is Iterated OverFor Loop[2]
Ex:has AttributeName[3]
Ex:belongs to ListSynset Lemmas[3]
Ex:has MethodName Method[3]
Is Member ofSynset[7]
Has PropertyName[8]
Belongs to ManySynset[9]

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.

23 undated
—
typebeam/9e885203-13b0-4f18-89db-79cab2460230
ex:Attribute
—
labelbeam/9e885203-13b0-4f18-89db-79cab2460230
lemma
—
typebeam/30196b02-e710-4de9-807e-b72cfda7e001
ex:Object
—
hasMethodbeam/30196b02-e710-4de9-807e-b72cfda7e001
ex:name
—
isIteratedOverbeam/30196b02-e710-4de9-807e-b72cfda7e001
ex:forLoop
—
typebeam/82dc87bd-74b8-4fb6-be5d-469ed934c86c
ex:Lemma
—
hasAttributebeam/82dc87bd-74b8-4fb6-be5d-469ed934c86c
ex:name
—
belongsToListbeam/82dc87bd-74b8-4fb6-be5d-469ed934c86c
ex:synset_lemmas
—
hasMethodbeam/82dc87bd-74b8-4fb6-be5d-469ed934c86c
ex:name_method
—
typebeam/6f825f15-5c97-4244-84f2-e40ee078d6ae
ex:LinguisticUnit
—
typebeam/4be5ccbb-c1b7-4c71-b494-78fd7c33ee6f
ex:Variable
—
typebeam/b27efc86-7008-4384-852a-049d06d255cb
ex:LemmaObject
—
hasAttributebeam/b27efc86-7008-4384-852a-049d06d255cb
ex:name
—
hasAttributebeam/5911aad5-31b8-481d-9758-9632ba044f91
name
—
isMemberOfbeam/5911aad5-31b8-481d-9758-9632ba044f91
ex:synset
—
typebeam/869acbd5-0cda-40b0-94b3-06d5699021f2
ex:linguistic-unit
—
has-propertybeam/869acbd5-0cda-40b0-94b3-06d5699021f2
ex:name
—
typebeam/57e2ea52-f5cb-4239-bf9f-3147a3b2efbc
ex:Lemma
—
belongsToManybeam/57e2ea52-f5cb-4239-bf9f-3147a3b2efbc
ex:synset
—
typebeam/eba347b2-a24e-4b7a-ab9b-f7cd8535ecce
ex:object
—
hasMethodbeam/eba347b2-a24e-4b7a-ab9b-f7cd8535ecce
ex:name
—
typebeam/edca9501-cce9-465a-87b1-ca97ba8c21a7
ex:Lemma
—
hasAttributebeam/edca9501-cce9-465a-87b1-ca97ba8c21a7
ex:name

References (11)

11 references
  1. [1]9e885203 13b0 4f18 89db 79cab24602302 facts
    ctx:claims/beam/9e885203-13b0-4f18-89db-79cab2460230
    • full textbeam-chunk
      text/plain1 KBdoc:beam/9e885203-13b0-4f18-89db-79cab2460230
      Show excerpt
      token_match=nlp.tokenizer.token_match) # Replace the default tokenizer with the custom one nlp.tokenizer = custom_tokenizer ``` ### Full Example Code Here is the full example code combining all the steps: ``…
  2. [2]30196b02 E710 4de9 807e B72cfda7e0013 facts
    ctx:claims/beam/30196b02-e710-4de9-807e-b72cfda7e001
    • full textbeam-chunk
      text/plain1 KBdoc:beam/30196b02-e710-4de9-807e-b72cfda7e001
      Show excerpt
      # Extract synonyms for each token synonyms = [] for token in tokens: # Use WordNet to get synonyms synsets = nltk.corpus.wordnet.synsets(token) for synset in synsets: for lemma in synset.lemma…
  3. [3]82dc87bd 74b8 4fb6 Be5d 469ed934c86c4 facts
    ctx:claims/beam/82dc87bd-74b8-4fb6-be5d-469ed934c86c
    • full textbeam-chunk
      text/plain1 KBdoc:beam/82dc87bd-74b8-4fb6-be5d-469ed934c86c
      Show excerpt
      nlp = spacy.load("en_core_web_sm") lemmatizer = WordNetLemmatizer() def get_wordnet_pos(treebank_tag): """Converts treebank POS tags to WordNet POS tags.""" if treebank_tag.startswith('J'): return wordnet.ADJ elif treeb…
  4. [4]6f825f15 5c97 4244 84f2 E40ee078d6ae1 fact
    ctx:claims/beam/6f825f15-5c97-4244-84f2-e40ee078d6ae
    • full textbeam-chunk
      text/plain1 KBdoc:beam/6f825f15-5c97-4244-84f2-e40ee078d6ae
      Show excerpt
      - **Contextual Relevance**: Consider using a context-aware approach to filter synonyms based on the context of the query. - **Dependency Parsing**: Use dependency parsing to better understand the relationships between words in the query. #…
  5. [5]4be5ccbb C1b7 4c71 B494 78fd7c33ee6f1 fact
    ctx:claims/beam/4be5ccbb-c1b7-4c71-b494-78fd7c33ee6f
  6. [6]B27efc86 7008 4384 852a 049d06d255cb2 facts
    ctx:claims/beam/b27efc86-7008-4384-852a-049d06d255cb
    • full textbeam-chunk
      text/plain1 KBdoc:beam/b27efc86-7008-4384-852a-049d06d255cb
      Show excerpt
      entities = [(ent.text, ent.label_) for ent in doc.ents] # Extract synonyms for each token synonyms = [] for token in tokens: pos = get_wordnet_pos(nltk.pos_tag([token])[0][1]) synsets = wordnet.synsets(t…
  7. [7]5911aad5 31b8 481d 9758 9632ba044f912 facts
    ctx:claims/beam/5911aad5-31b8-481d-9758-9632ba044f91
    • full textbeam-chunk
      text/plain1 KBdoc:beam/5911aad5-31b8-481d-9758-9632ba044f91
      Show excerpt
      2. **Download WordNet**: Download the WordNet data using NLTK. ```python import nltk nltk.download('wordnet') ``` 3. **Expand Synonyms Using WordNet**: ```python from nltk.corpus import wordnet as wn def expand_synony…
  8. [8]869acbd5 0cda 40b0 94b3 06d5699021f22 facts
    ctx:claims/beam/869acbd5-0cda-40b0-94b3-06d5699021f2
    • full textbeam-chunk
      text/plain1 KBdoc:beam/869acbd5-0cda-40b0-94b3-06d5699021f2
      Show excerpt
      elif term.endswith("ed"): return [term[:-2] + "ing"] # WordNet approach synonyms = set() for syn in wn.synsets(term): for lemma in syn.lemmas(): synonyms.add(lemma.name()) # NLP appr…
  9. [9]57e2ea52 F5cb 4239 Bf9f 3147a3b2efbc2 facts
    ctx:claims/beam/57e2ea52-f5cb-4239-bf9f-3147a3b2efbc
    • full textbeam-chunk
      text/plain1 KBdoc:beam/57e2ea52-f5cb-4239-bf9f-3147a3b2efbc
      Show excerpt
      tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') model = BertModel.from_pretrained('bert-base-uncased') def get_context_aware_synonyms(word, context_sentence): inputs = tokenizer(context_sentence, return_tensors='pt', pad…
  10. [10]Eba347b2 A24e 4b7a Ab9b F7cd8535ecce2 facts
    ctx:claims/beam/eba347b2-a24e-4b7a-ab9b-f7cd8535ecce
    • full textbeam-chunk
      text/plain1 KBdoc:beam/eba347b2-a24e-4b7a-ab9b-f7cd8535ecce
      Show excerpt
      To improve query rewriting accuracy, you can integrate synonym expansion using spaCy and a thesaurus like WordNet. ```python from nltk.corpus import wordnet def get_synonyms(word): synonyms = set() for syn in wordnet.synsets(word)…
  11. [11]Edca9501 Cce9 465a 87b1 Ca97ba8c21a72 facts
    ctx:claims/beam/edca9501-cce9-465a-87b1-ca97ba8c21a7

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