soda-inria/ken_embeddings
KEN: Relational Data Embeddings
This project helps data scientists and analysts enrich their datasets by providing pre-computed, comprehensive background information on common real-world entities like cities, companies, and people. You input a list of these entities, and it outputs a numerical vector (an 'embedding') for each, summarizing details from sources like Wikipedia. This saves significant manual effort in gathering and structuring external data to improve predictive models.
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Use this if you need to quickly add rich, relevant background information to your datasets for entities such as cities, companies, or people to improve the accuracy of your predictive models without tedious manual feature engineering.
Not ideal if your dataset primarily consists of highly specialized or niche entities not commonly found or well-documented in general knowledge bases like Wikipedia.
Stars
34
Forks
3
Language
Python
License
CC-BY-4.0
Category
Last pushed
Jan 02, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/embeddings/soda-inria/ken_embeddings"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
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