The Semantic Units Framework, a technology-agnostic representational approach to FAIR and CLEAR knowledge...

Area of Science:

• Knowledge Representation and Semantic Technologies
• Artificial Intelligence
• Information Science

Background:

• Limitations of current triple-centric and OWL-based knowledge graphs hinder complex meaning representation.
• Need for a more robust framework to handle nuanced scientific claims and argumentation.

Purpose of the Study:

• Introduce the Semantic Units Framework for technology-agnostic semantic modularization.
• Enable explicit representation of various claim types and epistemic stances.
• Facilitate structured scientific argumentation within a unified semantic model.

Main Methods:

• Treating statements and compound meaning structures as first-class semantic units with defined boundaries, identity, and epistemic status.
• Introducing instance-quantified resource categories (some-instance, most-instances, every-instance, all-instances) for explicit claim representation.
• Developing a conceptual foundation adaptable to existing data and knowledge representation technologies.

Main Results:

• The framework shifts focus from isolated triples to coherent, composable units of meaning.
• Enables representation of existential, prototypical, and universal claims, including negations and qualifications.
• Supports higher-order statements, epistemic stances, and structured scientific argumentation.

Conclusions:

• The Semantic Units Framework provides a reusable conceptual foundation for advanced knowledge representation.
• It supports both machine-actionable reasoning and human-interpretable knowledge representation.
• This approach advances semantic modularization beyond current limitations, fostering scientific generalization.