--- title: "OntoBook — paper by Touchent and Clergerie (2026)" description: "Medical coding means mapping clinical documents to codes such as CIM-10 diagnoses, CCAM procedures and ATC drugs. 1 reference." authors: ["Rian Touchent", "Éric de la Clergerie"] year: 2026 venue: "Proceedings of the Knowledge Graphs and Large Language Models Workshop (KG-LLM) @ LREC26" source: "https://aclanthology.org/2026.kallm-1.2/" doi: "10.63317/37ik5npnrvv6" licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)" licence_url: "https://creativecommons.org/licenses/by-nc/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/touchent-2026-ontobook-ontology-grounded-synthetic-textbooks/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/touchent-2026-ontobook-ontology-grounded-synthetic-textbooks/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/38/) / OntoBook: Ontology-Grounded Synthetic Textbooks for Medical Encoder Pretraining # OntoBook: Ontology-Grounded Synthetic Textbooks for Medical Encoder Pretraining **Authors:** [Rian Touchent](https://md.research.elamur.ai/free/en/authors/rian-touchent/) and [Éric de la Clergerie](https://md.research.elamur.ai/free/en/authors/eric-villemonte-de-la-clergerie/) · **Year:** 2026 · **Venue:** [Proceedings of the Knowledge Graphs and Large Language Models Workshop (KG-LLM) @ LREC26](https://md.research.elamur.ai/free/en/venues/kallm/) ([Knowledge Graphs and Large Language Models Workshop (KG-LLM) @ LREC26 2026](https://md.research.elamur.ai/free/en/venues/kallm/2026/)) · **Publisher:** [European Language Resources Association (ELRA)](https://md.research.elamur.ai/free/en/publishers/european-language-resources-association-elra/) · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/) **Links:** [Publisher's page](https://aclanthology.org/2026.kallm-1.2/) · [PDF](https://aclanthology.org/2026.kallm-1.2.pdf) · [DOI 10.63317/37ik5npnrvv6](https://doi.org/10.63317/37ik5npnrvv6) · [OpenAlex](https://openalex.org/W7166574078) **Citations:** 0 citations (OpenAlex, 2026-10-14) ## Summary Medical coding means mapping clinical documents to codes such as CIM-10 diagnoses, CCAM procedures and ATC drugs. It depends on how codes relate to each other: hierarchy, causation, differential diagnosis and exclusions. Ordinary clinical text rarely states these links, so encoders pretrained on it never learn them. OntoBook generates training text from the ontologies instead. It samples biased random walks over the ontology graphs so that rare semantic edges are actually traversed. Qwen3-235B then rewrites each walk as French textbook-style prose without adding or dropping facts. Finally, the 149M-parameter ModernCamemBERT is trained on this text with masked language modeling plus a second objective that predicts the relation between code pairs, with both objectives using the same documents. On French coding benchmarks the approach beats MLM-only pretraining, and the gain is largest on Distemist-FR. Using shared data for both objectives turns out to be essential. The work shows that structured knowledge alone, without aligned real-world text, can yield useful pretraining data for a lower-resource medical language. ## Key points - Gains over MLM-only pretraining: +2.5 micro-F1 on FRACCO and +8.0 micro-F1 on Distemist-FR. Cantemist-FR is the third benchmark. - Feeding the two objectives different data instead of the same textbooks lowers scores by about 30 points. - The released corpus has about 1.3M synthetic textbooks (~1.3B tokens): 402,328 from CIM-10, 762,958 from CCAM and 138,980 from ATC. Pretrained checkpoints are also released. - The ontologies come from the ANS terminology server (SMT) in RDF/OWL format. Only CIM-10 has non-hierarchical edges: 1,317 causal, 5,739 exclusion and 341 manifestation edges. - Walks are 8–20 steps long. CIM-10 uses five walk types: Etiologie, Diagnostic Différentiel, Codage Double, Syndrome and Cross-Chapter. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* ## Institutions - [Institut national de recherche en sciences et technologies du numérique](https://md.research.elamur.ai/free/en/labs/institut-national-de-recherche-en-sciences-et-technologies-du-numerique/) — Research institute, Le Chesnay-Rocquencourt, France · 84 works · 25,414 citations - [Sorbonne Université](https://md.research.elamur.ai/free/en/universities/sorbonne-universite/) — University, Paris, France · 32 works · 7,270 citations 📚 Shelves (6): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 16,039 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 15,891 works · [🧬 Biochemistry, Genetics and Molecular Biology](https://md.research.elamur.ai/free/en/shelves/biochemistry-genetics-and-molecular-biology/) · 234 works · [🧬 Molecular Biology](https://md.research.elamur.ai/free/en/shelves/biochemistry-genetics-and-molecular-biology/molecular-biology/) · 231 works 🧭 Topics (1): [🕸️ Knowledge graphs & ontologies](https://md.research.elamur.ai/free/en/topics/knowledge-graphs/) · 192 works ## Cite Cite as: Rian Touchent and Éric de la Clergerie. 2026. OntoBook: Ontology-Grounded Synthetic Textbooks for Medical Encoder Pretraining. Proceedings of the Knowledge Graphs and Large Language Models Workshop (KG-LLM) @ LREC26. The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{touchent2026ontobook, title = {{OntoBook: Ontology-Grounded Synthetic Textbooks for Medical Encoder Pretraining}}, author = {Rian Touchent and Éric {de la Clergerie}}, booktitle = {Proceedings of the Knowledge Graphs and Large Language Models Workshop (KG-LLM) @ LREC26}, year = {2026}, doi = {10.63317/37ik5npnrvv6}, url = {https://doi.org/10.63317/37ik5npnrvv6} } ``` - [← previous Onomasiological Sense Alignment Across Dialect Dictionaries. A Taxonomy-Constrained LLM Classification](https://md.research.elamur.ai/free/en/papers/mederake-2026-onomasiological-sense-alignment-across-dialect/) - [next → OntoGuard: Enforcing Action Admissibility for LLM Agents in Complex Interactive Environments](https://md.research.elamur.ai/free/en/papers/liu-2026-ontoguard-enforcing-action-admissibility-llm/) - [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/38/) ## References 1 references, 1 of them with a card of ours. ↗ leads to the work. 1. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. [BERT: Pre-training of deep bidirectional transformers for language understanding](https://md.research.elamur.ai/free/en/papers/devlin-2019-bert-pre-training-deep-bidirectional/). In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171–4186, Minneapolis, Minnesota. Association for Computational Linguistics. *Updated 1 November 2026*