[D] Beyond embeddings for natural language search

Paraphrasing some threads of the past few months, the bottleneck in QA-Retrieval seems to be retrieval, and vector search with embeddings does not, by itself, seem to be good enough for "production".

I recently came across [OpenEvidence](https://www.openevidence.com/) (not to be confused with [Open Evidence](https://open-evidence.com/)), which seems to do retrieval and references [pretty well](https://www.openevidence.com/ask/8bcf49ba-4103-4eb5-8c70-3d045897cef8). Digging into some of their [published papers](https://arxiv.org/abs/2302.08091) and [their LinkedIn page](https://www.linkedin.com/company/openevidence/about/), it looks like they built an ontology out of PubMed,

>By analyzing medical text and **extracting …

beyond biomedical embeddings good history language machinelearning medical natural natural language paraphrasing production relations retrieval science search text threads vector vector search