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        <namePart type="given">Renato</namePart>
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        <namePart type="given">Hsien-chin</namePart>
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        <namePart type="given">Shutong</namePart>
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    <abstract>Large language models (LLMs) are widely used as general-purpose knowledge sources, but they rely on parametric knowledge, limiting explainability and trustworthiness. In task-oriented dialogue (TOD) systems, this separation is explicit, using an external database structured by an explicit ontology to ensure explainability and controllability. However, building such ontologies requires manual labels or supervised training. We introduce TeQoDO: a Text-to-SQL task-oriented Dialogue Ontology construction method. Here, an LLM autonomously builds a TOD ontology from scratch using only its inherent SQL programming capabilities combined with concepts from modular TOD systems provided in the prompt. We show that TeQoDO outperforms transfer learning approaches, and its constructed ontology is competitive on a downstream dialogue state tracking task. Ablation studies demonstrate the key role of modular TOD system concepts. TeQoDO also scales to allow construction of much larger ontologies, which we investigate on a Wikipedia and arXiv dataset. We view this as a step towards broader application of ontologies.1</abstract>
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%0 Journal Article
%T Text-to-SQL Task-oriented Dialogue Ontology Construction
%A Vukovic, Renato
%A van Niekerk, Carel
%A Heck, Michael
%A Ruppik, Benjamin
%A Lin, Hsien-chin
%A Feng, Shutong
%A Lubis, Nurul
%A Gašić, Milica
%J Transactions of the Association for Computational Linguistics
%D 2026
%V 14
%I MIT Press
%C Cambridge, MA
%F vukovic-etal-2026-text
%X Large language models (LLMs) are widely used as general-purpose knowledge sources, but they rely on parametric knowledge, limiting explainability and trustworthiness. In task-oriented dialogue (TOD) systems, this separation is explicit, using an external database structured by an explicit ontology to ensure explainability and controllability. However, building such ontologies requires manual labels or supervised training. We introduce TeQoDO: a Text-to-SQL task-oriented Dialogue Ontology construction method. Here, an LLM autonomously builds a TOD ontology from scratch using only its inherent SQL programming capabilities combined with concepts from modular TOD systems provided in the prompt. We show that TeQoDO outperforms transfer learning approaches, and its constructed ontology is competitive on a downstream dialogue state tracking task. Ablation studies demonstrate the key role of modular TOD system concepts. TeQoDO also scales to allow construction of much larger ontologies, which we investigate on a Wikipedia and arXiv dataset. We view this as a step towards broader application of ontologies.1
%R 10.1162/tacl.a.689
%U https://aclanthology.org/2026.tacl-1.40/
%U https://doi.org/10.1162/tacl.a.689
%P 893-917

Markdown (Informal)

[Text-to-SQL Task-oriented Dialogue Ontology Construction](https://aclanthology.org/2026.tacl-1.40/) (Vukovic et al., TACL 2026)

• Text-to-SQL Task-oriented Dialogue Ontology Construction (Vukovic et al., TACL 2026)

ACL

• Renato Vukovic, Carel van Niekerk, Michael Heck, Benjamin Ruppik, Hsien-chin Lin, Shutong Feng, Nurul Lubis, and Milica Gašić. 2026. Text-to-SQL Task-oriented Dialogue Ontology Construction. Transactions of the Association for Computational Linguistics, 14:893–917.