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- Improvements to table label detection · 1 ✖
id | html_url | issue_url | node_id | user | created_at | updated_at ▲ | author_association | body | reactions | issue | performed_via_github_app |
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1264737290 | https://github.com/simonw/datasette/issues/485#issuecomment-1264737290 | https://api.github.com/repos/simonw/datasette/issues/485 | IC_kwDOBm6k_c5LYlwK | simonw 9599 | 2022-10-02T21:29:59Z | 2022-10-02T21:29:59Z | OWNER | To clarify: the feature this issue is talking about relates to the way Datasette automatically displays foreign key relationships, for example on this page: https://github-to-sqlite.dogsheep.net/github/commits Each of those columns is a foreign key to another table. The link text that is displayed there comes from the "label column" that has either been configured or automatically detected for that other table. I wonder if this could be handled with a tiny machine learning model that's trained to help pick the best label column? Inputs to that model could include:
Output would be the most likely label column, or some indicator that no likely candidates had been found. My hunch is that this would be better solved using a few extra heuristics rather than by training a model, but it does feel like an interesting opportunity to experiment with a tiny ML model. Asked for tips about this on Twitter: https://twitter.com/simonw/status/1576680930680262658 |
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Improvements to table label detection 447469253 |
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