CES Simplifies OWL Class Expressions for Improved Ontology Management

Alkid Baci, N'Dah Jean Kouagou, Caglar Demir, Axel-Cyrille Ngonga Ngomo· August 20, 2026 View original

Key takeaways

  • CES simplifies complex OWL class expressions syntactically.
  • It preserves formal semantics while reducing representational complexity.
  • Simplification improves reasoning efficiency and human readability.
  • The algorithm is implemented in the open-source OWLAPY framework.

Who benefits

Data ManagementAI/ML ResearchSemantic WebHealthcareFinance

Summary

Class Expression Simplifier (CES) is a new algorithm that syntactically simplifies complex OWL class expressions while preserving formal semantics. It reduces representational complexity, making ontologies more interpretable, efficient for reasoning, and easier to maintain.

Learning class expressions in OWL often results in complex and difficult-to-interpret representations, hindering effective reasoning and maintenance of ontologies. This complexity can be mitigated by applying theoretically sound simplification principles. Researchers have introduced the Class Expression Simplifier (CES), a novel algorithm designed for the syntactic simplification of class expressions within Description Logics (DL).CES operates by systematically applying rewriting rules to eliminate redundancies and identify simpler, yet logically equivalent, expressions. Its primary goal is to reduce representational complexity and improve human readability without altering the formal semantics or logical entailments of the original expressions. This ensures that the simplified ontologies remain functionally identical but are far more accessible.The effectiveness of CES was evaluated using class expressions derived from two medium-sized ontologies. The results demonstrated measurable improvements in reasoning efficiency and a significant reduction in verbosity. This work contributes to making ontology-driven applications more accessible, maintainable, and scalable, with direct implications for areas such as knowledge graph construction, semantic search, and Web-scale reasoning. CES is available as part of the open-source Python framework OWLAPY.

Why it matters

Professionals dealing with knowledge graphs, semantic web technologies, or complex data models can use CES to improve the clarity, maintainability, and reasoning efficiency of their ontologies, leading to more robust and scalable systems.

How to implement this in your domain

  1. 1Assess the complexity of existing OWL ontologies or knowledge graphs.
  2. 2Integrate the CES algorithm (via OWLAPY) into ontology development workflows.
  3. 3Apply CES to simplify learned or manually created class expressions.
  4. 4Measure the impact of simplification on reasoning performance and human interpretability.
  5. 5Educate team members on best practices for creating and maintaining simplified ontologies.

Original post by Alkid Baci, N'Dah Jean Kouagou, Caglar Demir, Axel-Cyrille Ngonga Ngomo

"arXiv:2608.18899v1 Announce Type: new Abstract: Class expression learning often produces complex OWL class expressions that are difficult to interpret and reason over. However, by following theoretically grounded simplification principles, this complexity can be reduced. In this…"

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Originally posted by Alkid Baci, N'Dah Jean Kouagou, Caglar Demir, Axel-Cyrille Ngonga Ngomo on X · view source

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