Challenges in Ontology Competency Question Verification Identified

Anna Sofia Lippolis, Mohammad Javad Saeedizade, Robin Keskis\"arkk\"a, Aldo Gangemi, Eva Blomqvist, Andrea Giovanni Nuzzolese· June 24, 2026 View original

Summary

This paper investigates the difficulties in verifying Competency Questions (CQs) for ontologies, a process often time-consuming and error-prone due to linguistic nuances and complex alignment. It highlights the necessity of tools to refine CQs to prevent ambiguity and complexity in ontology engineering.

Ontology engineering relies on Competency Questions (CQs) to ensure an ontology accurately models its intended purpose. However, the process of verifying these CQs is frequently hampered by the intricate interpretation of natural language and the precise mapping required to formal ontology constructs. This complexity can lead to inconsistencies in modeling decisions and verification outcomes. Researchers conducted an experiment with 19 participants using an LLM assistant for CQ verification across 20 tasks. The findings underscore that ambiguities and excessive complexity within CQs significantly complicate the verification process. The study concludes that a crucial step in the ontology engineering workflow should be the refinement of CQs before their publication. This proactive measure would mitigate issues arising from unclear or overly complex questions in later stages, thereby improving the efficiency and accuracy of ontology development.

Why it matters

Professionals involved in data modeling, knowledge representation, or AI system development using ontologies need to understand these challenges to improve the reliability and efficiency of their systems. Addressing CQ quality can prevent costly errors and rework in complex AI projects.

How to implement this in your domain

  1. 1Integrate automated tools for linguistic analysis and ambiguity detection into CQ development workflows.
  2. 2Establish clear guidelines and best practices for writing unambiguous and concise Competency Questions.
  3. 3Conduct pilot verification rounds with diverse stakeholders to identify potential misinterpretations early.
  4. 4Train ontology engineers and domain experts on common pitfalls in CQ formulation and verification.
  5. 5Leverage LLM-based assistants for initial CQ refinement and consistency checks before formal verification.

Who benefits

Data ManagementAI DevelopmentHealthcareFinanceLegal

Key takeaways

  • Competency Question (CQ) verification is a critical but often flawed step in ontology engineering.
  • Linguistic ambiguity and complexity in CQs lead to inconsistent ontology modeling.
  • LLM assistants can support CQ verification but highlight the need for pre-publication refinement.
  • Proactive CQ refinement is essential to enhance the accuracy and efficiency of ontology development.

Original post by Anna Sofia Lippolis, Mohammad Javad Saeedizade, Robin Keskis\"arkk\"a, Aldo Gangemi, Eva Blomqvist, Andrea Giovanni Nuzzolese

"arXiv:2606.24619v1 Announce Type: new Abstract: Competency Questions (CQs) are the central component of CQ-verification, an established process in which an ontology is evaluated against a set of natural language questions to determine whether the intended purpose of the ontology…"

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Originally posted by Anna Sofia Lippolis, Mohammad Javad Saeedizade, Robin Keskis\"arkk\"a, Aldo Gangemi, Eva Blomqvist, Andrea Giovanni Nuzzolese on X · view source

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