From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-09.)
Build Index has 33 facts recorded in Dontopedia across 4 references, with 3 live disagreements.
Mostly:rdf:type(4), rdfs:label(3), returns(3)
returnshasParameterreturnsIndexcreatesIndexdesignedForcallsMethodextractsFromtakesInputunpackingAssignmentOther 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.
createdByCreated by(2)ex:tfidf_matrixex:vectorizerconsistsOfConsists of(1)ex:multilingual_systemfeedsIntoFeeds Into(1)ex:get_embeddingsThe 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.
| Predicate | Value | Ref |
|---|---|---|
| Caching Mechanism | cache | [1] |
| Second Return Value | Tfidf Matrix | [1] |
| First Return Value | Vectorizer | [1] |
| Returns Tuple | true | [1] |
| Caches Results | true | [1] |
| Second Parameter | Vectorizer | [1] |
| First Parameter | Preprocessed Documents | [1] |
| Parameter Count | 2 | [1] |
| Called After | preprocess_document | [1] |
| Returns Multiple Values | 2 | [1] |
| Avoids | recomputing_index | [1] |
| Result Is Cached | true | [1] |
| Parameter | Preprocessed Documents | [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.
doc:beam/719c7dfe-90ed-419b-85d5-cac7ba365816# Load multilingual model and tokenizer model_name = 'bert-base-multilingual-cased' tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModel.from_pretrained(model_name) def get_embeddings(texts): inputs = tokenizer(texts…
doc:beam/b2fa8237-a2ba-45f1-b609-1096fd02ce18vectorizer = 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…
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