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.
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound 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)
- Similarity List Computation
similarity-list-computation
computedWithComputed With(1)
- Dense Scores
ex:dense-scores
usesUses(1)
- Hybrid Ranking Function
ex:hybrid-ranking-function
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.
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Vector Operation | [1] |
| Rdf:type | Mathematical Operation | [2] |
| Operates on | Query Vector | [1] |
| Operates on | Database 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.
References (2)
ctx:claims/beam/5278119f-c632-4b91-b193-f1e7bddf1e64- full textbeam-chunktext/plain1 KB
doc:beam/5278119f-c632-4b91-b193-f1e7bddf1e64Show 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 …
ctx:claims/beam/b4174542-e9f5-41d0-809f-ec6511b667bb- full textbeam-chunktext/plain1 KB
doc:beam/b4174542-e9f5-41d0-809f-ec6511b667bbShow 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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