Towards Ontology-Based Descriptions of Conversations with Qualitatively-Defined Concepts

• URL: http://arxiv.org/abs/2509.04926v1
• Date: Fri, 05 Sep 2025 08:44:27 GMT
• Title: Towards Ontology-Based Descriptions of Conversations with Qualitatively-Defined Concepts
• Authors: Barbara Gendron, Gaël Guibon, Mathieu D'aquin,
• Abstract summary: This work proposes an ontology-based approach to formally define conversational features that are typically qualitative in nature.<n>We apply this framework to the task of proficiency-level control in conversations, using CEFR language proficiency levels as a case study.<n> Experimental results demonstrate that our approach provides consistent and explainable proficiency-level definitions, improving transparency in conversational AI.
• Score: 2.748993665644782
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The controllability of Large Language Models (LLMs) when used as conversational agents is a key challenge, particularly to ensure predictable and user-personalized responses. This work proposes an ontology-based approach to formally define conversational features that are typically qualitative in nature. By leveraging a set of linguistic descriptors, we derive quantitative definitions for qualitatively-defined concepts, enabling their integration into an ontology for reasoning and consistency checking. We apply this framework to the task of proficiency-level control in conversations, using CEFR language proficiency levels as a case study. These definitions are then formalized in description logic and incorporated into an ontology, which guides controlled text generation of an LLM through fine-tuning. Experimental results demonstrate that our approach provides consistent and explainable proficiency-level definitions, improving transparency in conversational AI.

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