Similarity isn't relevance: the hard part of semantic search
Here's a dirty secret of search: "the closest match" and "the most useful result" are not the same thing. Return the mathematically nearest document and you'll often hand someone something technically related and practically useless. Relevance is a harder problem than similarity — and it's where good search is won or lost. Getting that right was the core challenge in the GovernAI Research Atlas ,…
Semantic search can deliver results that are semantically near a query, but they may not always be the most useful. The closest match in mathematical terms does not guarantee relevance. Relevance is a more complex problem than similarity. Getting this right is the core challenge in platforms like the GovernAI Research Atlas. The Atlas is a semantic discovery platform that unifies research across papers, repositories, and policy.
While similarity is a superpower of semantic search, raw nearest-neighbor retrieval has a blind spot. The vector-closest result might be tangentially related, while the truly useful result sits slightly further away. Distance in embedding space is a good proxy for relevance, but it's not the whole story. Ranking is crucial. The Atlas uses ChromaDB vector search with Sentence-Transformer embeddings across sources like OpenAlex and GitHub.
On top of retrieval sits a custom relevance score that decides what surfaces first. Retrieval pulls in semantically relevant candidates quickly from a large and diverse corpus. Ranking reorders these candidates based on actual usefulness because the goal is to surface the right result first. Unifying papers, code, and governance material into a single relevance-ordered experience, where discovery crosses formats instead of siloing them, is what makes the Atlas an interesting search system. Retrieval gets the attention, but ranking delivers the results.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
