@inproceedings{vuth-etal-2026-ontology,
    title = "Ontology-Guided Synthetic Data Generation for Low-Resource Information Extraction: A Case Study in {IT} Heritage Domain",
    author = "Vuth, Nakanyseth  and
      Poncet, Emrick  and
      S{\'e}rasset, Gilles  and
      Schwab, Didier  and
      Djambian, Caroline  and
      Lecouteux, Benjamin",
    editor = "S{\'e}rasset, Gilles  and
      Gkirtzou, Katerina  and
      Cochez, Michael  and
      Kalo, Jan-Christoph",
    booktitle = "Proceedings of the Knowledge Graphs and Large Language Models Workshop ({KG}-{LLM}) @ {LREC}26",
    month = may,
    year = "2026",
    address = "Palma, Mallorca (Spain)",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/paragraph-normalization/2026.kallm-1.8/",
    doi = "10.63317/2ze7afc3v7zy",
    pages = "73--81",
    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."
}