An embedding maps content to a numeric vector so a distance measure can approximate similarity. Nearby vectors may still differ in truth, authority, time, or required access.

Embeddings Encode Similarity, Not Meaning Guarantees

A graph neural network aggregates features across related nodes to learn representations or predictions. Its output depends on edge semantics, neighborhood sampling, depth, and training labels.

Graph Neural Networks Learn From Neighborhood Structure

In this situation—finding policies similar to a user's question—which choice best applies “Embeddings Encode Similarity, Not Meaning Guarantees”?

Ranking orders candidates using signals such as lexical match, vector similarity, authority, freshness, graph distance, and access. The weights should reflect the task rather than one universal score.

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