Dontopedia

dot product

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

dot product has 5 facts recorded in Dontopedia across 2 references, with 2 live disagreements.

5 facts·2 predicates·2 sources·2 in dispute
Maturity scale raw canonical shape-checked rule-derived certified

Inbound mentions (3)

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.

appliesApplies(1)

computedWithComputed With(1)

usesUses(1)

Other facts (4)

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.

4 facts
PredicateValueRef
Rdf:typeVector Operation[1]
Rdf:typeMathematical Operation[2]
Operates onQuery Vector[1]
Operates onDatabase Vector[1]

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/5278119f-c632-4b91-b193-f1e7bddf1e64
ex:VectorOperation
operatesOnbeam/5278119f-c632-4b91-b193-f1e7bddf1e64
ex:query-vector
operatesOnbeam/5278119f-c632-4b91-b193-f1e7bddf1e64
ex:database-vector
typebeam/b4174542-e9f5-41d0-809f-ec6511b667bb
ex:MathematicalOperation
labelbeam/b4174542-e9f5-41d0-809f-ec6511b667bb
dot product

References (2)

2 references
  1. ctx:claims/beam/5278119f-c632-4b91-b193-f1e7bddf1e64
    • full textbeam-chunk
      text/plain1 KBdoc:beam/5278119f-c632-4b91-b193-f1e7bddf1e64
      Show excerpt
      # Calculate the similarity between the query vector and each vector in the database similarities = [np.dot(query_vector, vector) for vector in self.vectors] # Return the indices of the top 10 most similar vectors
  2. ctx: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

See also

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