From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-09.)
Combined Scores has 24 facts recorded in Dontopedia across 7 references, with 3 live disagreements.
Mostly:rdf:type(7), rdfs:label(4), computed from(2)
computedFromusesrdfs:labelcalculationweightSuminput1input0weightDenseweightSparsecomputedByOther 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.
computesComputes(1)hybrid_rankingcontainsContains(1)ex:CombinedRankingderivedFromDerived From(1)ex:combined_indicesinputInput(1)ex:combined_top_indicesperformsTensorAdditionPerforms Tensor Addition(1)ex:score-fusion-servicereturnsReturns(1)ex:hybrid_sparse_dense_retrievalThe 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 |
|---|---|---|
| Type | numpy_array | [3] |
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/8099970e-f2d8-437f-874b-e1c72a22eeb0Assuming 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…
doc:beam/b4174542-e9f5-41d0-809f-ec6511b667bbdense_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…
doc:beam/a473407e-8449-4e78-89b6-989e8d589870query = 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…
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…
Dontopedia is in a read-only public launch. Follow the references and disputed branches now; contributions will open after durable identity and moderation are in place.