Rethinking OWL Expressivity: Semantic Units for FAIR and Cognitively Interoperable Knowledge Graphs Why OWLs don't have to understand everything they say

TL;DR

This paper introduces a modular framework for semantic knowledge graphs that enhances expressivity, interoperability, and cognitive accessibility by using semantic units and new representational resources, addressing OWL limitations.

Contribution

It proposes a novel semantic modularization approach with new resource types, enabling integration across logical frameworks and addressing core OWL modeling limitations.

Findings

Addresses twelve core OWL/RDF limitations

Enables reasoning across different logical frameworks

Improves cognitive accessibility for domain experts

Abstract

Semantic knowledge graphs are foundational to implementing the FAIR Principles, yet RDF/OWL representations often lack the semantic flexibility and cognitive interoperability required in scientific domains. We present a novel framework for semantic modularization based on semantic units (i.e., modular, semantically coherent subgraphs enhancing expressivity, reusability, and interpretability), combined with four new representational resource types (some-instance, most-instances, every-instance, all-instances) for modelling assertional, contingent, prototypical, and universal statements. The framework enables the integration of knowledge modelled using different logical frameworks (e.g., OWL, First-Order Logic, or none), provided each semantic unit is internally consistent and annotated with its logic base. This allows, for example, querying all OWL 2.0-compliant units for reasoning purposes while preserving the full graph for broader knowledge discovery. Our framework addresses twelve core limitations of OWL/RDF modeling, including negation, cardinality, complex class axioms, conditional and directive statements, and logical arguments, while improving cognitive accessibility for domain experts. We provide schemata and translation patterns to demonstrate semantic interoperability and reasoning potential, establishing a scalable foundation for constructing FAIR-aligned, semantically rich knowledge graphs.