About

Ontological modeling and analysis have seen growing adoption in Model-Based Systems Engineering, Software Engineering, and related fields such as Digital Twins. Modern engineering systems demand the ability to capture, integrate, and reason over heterogeneous domain knowledge, which is a problem that ontological approaches address well and one that can be further leveraged by recent advances in AI and ML, including Large Language Models. Even so, deeply connecting ontological modeling with systems and software engineering practice remains an open and important research challenge.

The second onto:Nexus workshop continues the work of its predecessor by gathering researchers, practitioners, and tool developers working at this intersection. The primary objective is to facilitate discussion and generate new ideas, particularly across participants from different domains and backgrounds.

A second objective is to present and discuss novel and ongoing work in ontological modeling and analysis. Given the renewed interest in ontological techniques across many disciplines, the workshop welcomes early-stage research, foundational contributions, and applications to real-world case studies.

Important Dates

• Friday June 26, 2026 Abstract submission
• Friday July 3, 2026 Paper submission
• Friday July 31, 2026 Notification of acceptance
• Friday August 14, 2026 Camera-ready submission

Topics of Interest

• Ontological modeling methods, analysis techniques, and processes formodel-based software and systems engineering
• Best practices, guidelines, case studies, and benchmarks for ontological modeling
• Ontology development through collaboration, machine learning, or otherautomated approaches
• Tools and frameworks supporting ontological modeling and analysis
• Empirical studies on the use of ontologies in MBSE
• Connections between ontologies and formal methods
• Organizational and governance aspects of ontology management
• Semantic interoperability in software and systems engineering
• AI-assisted ontological modeling and analysis, and the use of ontologies tosupport AI systems
• Human-in-the-loop approaches for integrating human knowledge and behavior into ontological modeling