OntoScope: Using a Divergent-Convergent Interaction Framework to Support LLM-based Ontology Scoping

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Human-LLM CollaborationExplainable AI (XAI)Interactive Data VisualizationHCI ResearchersAI/ML Researchers & EngineersData Scientists & Analysts

An ontology is a formal, explicit specification of a shared conceptualization that, with problem‑solving and reasoning methods, supports efficient semantic technology development. In ontology engineering, Competency Questions (CQs) capture functional requirements that define an ontology's application domain. Auditing this domain scope with CQs is challenging because in nature, there are no clear domain boundaries, and ontology engineers must then decide which subdomains to cover (horizontal coverage) and how much detail to model (vertical granularity) in an ontology. LLM‑based systems can generate many candidate CQs to guide these decisions, but current tools underuse this potential: they lack support for users' divergent (lateral) and convergent (vertical) thinking in a visualized CQs space organized by coverage and granularity. As a result, users struggle to systematically decide which CQs to adopt, discard, or refine. We propose an interaction framework that fills this gap, demonstrated through OntoScope, an LLM‑based interactive system, and a user study with 15 ontology engineers. To our knowledge, this is the first validated interaction framework with an LLM‑based system that helps ontology engineers audit domain boundaries and unifies fragmented, expert‑driven ontology scoping practices into a coherent, accessible approach. More broadly, it shows how LLM‑based systems can transparently and accountably support a wider range of knowledge‑intensive tasks.

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https://hci.top/en/papers/iui/226661/2026

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Source
IUI
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Year
2026
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Best Paper
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Authors
3 authors
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Subtopics
Human-LLM Collaboration, Explainable AI (XAI), Interactive Data Visualization
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HCI Researchers, AI/ML Researchers & Engineers, Data Scientists & Analysts
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Abstract only
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