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        <title>Ontology-Guided Synthetic Data Generation for Low-Resource Information Extraction: A Case Study in IT Heritage Domain</title>
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    <abstract>Information Extraction (IE) in specialized domains often suffers from a severe cold-start problem due to the high cost of expert annotation. Recent Reverse-IE approaches leverage knowledge graphs to generate synthetic training corpora, but typically assume the availability of an existing knowledge base. In this work, we propose an ontology-driven pipeline for synthetic supervision that removes this requirement. Starting from a formal domain ontology, we introduce a stochastic motif sampling strategy that constructs schema-consistent Knowledge Graph structures with controllable topology, which are then verbalized into natural language. This ontology-first formulation also allows direct control over the data generation process, enabling oversampling of underrepresented entity types or relation patterns. Applied to the IT Heritage domain, our approach produces a fully labeled NER/RE corpus without large-scale manual annotation. Evaluation in a low-resource setting shows that while the synthetic corpus lacks the linguistic diversity of gold data, its scalability produces training sets large enough to alleviate the cold-start problem, making ontology-guided motif generation a practical strategy for domains where gold annotation is limited.</abstract>
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%0 Conference Proceedings
%T Ontology-Guided Synthetic Data Generation for Low-Resource Information Extraction: A Case Study in IT Heritage Domain
%A Vuth, Nakanyseth
%A Poncet, Emrick
%A Sérasset, Gilles
%A Schwab, Didier
%A Djambian, Caroline
%A Lecouteux, Benjamin
%Y Sérasset, Gilles
%Y Gkirtzou, Katerina
%Y Cochez, Michael
%Y Kalo, Jan-Christoph
%S Proceedings of the Knowledge Graphs and Large Language Models Workshop (KG-LLM) @ LREC26
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F vuth-etal-2026-ontology
%X Information Extraction (IE) in specialized domains often suffers from a severe cold-start problem due to the high cost of expert annotation. Recent Reverse-IE approaches leverage knowledge graphs to generate synthetic training corpora, but typically assume the availability of an existing knowledge base. In this work, we propose an ontology-driven pipeline for synthetic supervision that removes this requirement. Starting from a formal domain ontology, we introduce a stochastic motif sampling strategy that constructs schema-consistent Knowledge Graph structures with controllable topology, which are then verbalized into natural language. This ontology-first formulation also allows direct control over the data generation process, enabling oversampling of underrepresented entity types or relation patterns. Applied to the IT Heritage domain, our approach produces a fully labeled NER/RE corpus without large-scale manual annotation. Evaluation in a low-resource setting shows that while the synthetic corpus lacks the linguistic diversity of gold data, its scalability produces training sets large enough to alleviate the cold-start problem, making ontology-guided motif generation a practical strategy for domains where gold annotation is limited.
%R 10.63317/2ze7afc3v7zy
%U https://aclanthology.org/2026.kallm-1.8/
%U https://doi.org/10.63317/2ze7afc3v7zy
%P 73-81

Markdown (Informal)

[Ontology-Guided Synthetic Data Generation for Low-Resource Information Extraction: A Case Study in IT Heritage Domain](https://aclanthology.org/2026.kallm-1.8/) (Vuth et al., KaLLM 2026)

• Ontology-Guided Synthetic Data Generation for Low-Resource Information Extraction: A Case Study in IT Heritage Domain (Vuth et al., KaLLM 2026)

ACL

• Nakanyseth Vuth, Emrick Poncet, Gilles Sérasset, Didier Schwab, Caroline Djambian, and Benjamin Lecouteux. 2026. Ontology-Guided Synthetic Data Generation for Low-Resource Information Extraction: A Case Study in IT Heritage Domain. In Proceedings of the Knowledge Graphs and Large Language Models Workshop (KG-LLM) @ LREC26, pages 73–81, Palma, Mallorca (Spain). ELRA Language Resources Association (ELRA).