Hello dear Wikidata enthusiasts,
I'm one of the authors of a recent paper where we made heavy use of Wikidata to create a large-scale image-text dataset for training CLIP vision-language models. I thought this list might find the approach interesting.
What we did: We used SPARQL queries over the subclass-of (P279) and parent-taxon (P171) hierarchies to extract ~135k visual entities from 21 super-entity categories (animals, plants, tools, buildings, etc.). For each entity we collected its name, description, aliases, and sitelink count. We then extracted attributes from Wikidata related to color, partonomy, behavior, and other aspects. These entities and attributes were used to generate search queries for downloading images from the web.
The resulting EntityNet dataset contains 33M images, 46M text descriptions, and 613k text labels linked back to Wikidata entities. The dataset and trained models are openly available on HuggingFace: https://huggingface.co/datasets/lmb-freiburg/entitynet https://huggingface.co/datasets/lmb-freiburg/entitynet
A key finding was that training on a mix of Wikidata's structured entity information and noisy web alt-texts works better than either alone. The knowledge graph metadata genuinely improves model quality.
Paper: https://arxiv.org/abs/2505.02746 https://arxiv.org/abs/2505.02746 | Code: https://github.com/lmb-freiburg/entitynet https://github.com/lmb-freiburg/entitynet
Happy to answer any questions about how we used Wikidata or discuss potential improvements to the entity extraction.