OntoBook: Ontology-Grounded Synthetic Textbooks for Medical Encoder Pretraining
We present OntoBook, a method that converts medical ontology structure into pretraining signal for encoder language models. Our approach has three stages: random walks through ontology graphs capture hierarchical and causal relations between medical codes, a large language model reformulates these walks into fluent textbook-style prose, and the resulting text is used to train ModernCamemBERT, a 149M-parameter French encoder, with two objectives on the same data: masked language modeling and relation prediction between code pairs. On three French medical coding benchmarks (FRACCO, Cantemist-FR, Distemist-FR), OntoBook achieves significant improvements over MLM-only pretraining, with +2.5 m...
| Authors | Rian Touchent, Éric de la Clergerie |
|---|---|
| Primary category | cs.AI |
| Published | 21 July 2026 |
| Last updated at source | 21 July 2026 |
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