OntoScope: Using a Divergent-Convergent Interaction Framework to Support LLM-based Ontology Scoping
Best PaperAuthors
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.
Research Questions / Practical Problems
Question signals indexed for this paper.
- 86%
Lexara: A User-Centered Toolkit for Evaluating Large Language Models for Conversational Visual Analytics
CHI '26· Human-LLM Collaboration +2
- 75%
Interactive Reasoning: Visualizing and Controlling Chain-of-Thought Reasoning in Large Language Models
IUI '26· Human-LLM Collaboration +3
- 71%
Gamut: A Design Probe to Understand How Data Scientists Understand Machine Learning Models
CHI '19· Explainable AI (XAI) +2
- 71%
Supporting Sensemaking of Large Language Model Outputs at Scale
CHI '24· Human-LLM Collaboration +2
- 71%
How Do Analysts Understand and Verify AI-Assisted Data Analyses?
CHI '24· Human-LLM Collaboration +2
- 71%
More Isn't Always Better: Balancing Decision Accuracy and Conformity Pressures in Multi-AI Advice
CHI '26· Human-LLM Collaboration +2
- 71%
TSEditor: Interactive Time Series Editing for Privacy Preservation
CHI '26· Privacy Perception & Decision-Making +2
- 71%
Sensing What Surveys Miss: Understanding and Personalizing Proactive LLM Support by User Modeling
CHI '26· Human-LLM Collaboration +2
- 71%
Debugging Defective Visualizations: Empirical Insights Informing a Human-AI Co‑Debugging System
CHI '26· Interactive Data Visualization +2
- 71%
PleaSQLarify: Visual Pragmatic Repair for Natural Language Database Querying
CHI '26· Human-LLM Collaboration +2
Based on Jaccard similarity of research subtopics & professions (≥60%)