Suggestions for extending the FAIR Principles based on a linguistic perspective on semantic interoperability.

Area of Science:

• Data Science
• Information Science
• Linguistics

Background:

• The FAIR Guiding Principles lack detailed specifications for semantic interoperability.
• Effective machine and human communication of data requires preserved meaning and reference.
• Existing frameworks do not fully address the linguistic nuances of data interpretation.

Purpose of the Study:

• To propose a conceptual model for semantic interoperability grounded in linguistic principles.
• To identify necessary components for reliable data communication and interpretation.
• To suggest enhancements to the FAIR Guiding Principles for improved semantic interoperability.

Main Methods:

• Adopting a linguistic perspective to analyze semantic interoperability.
• Drawing parallels between textual information structures and data structures.
• Developing a conceptual model encompassing terminological and propositional interoperability.

Main Results:

• A conceptual model for semantic interoperability is proposed, including intensional/extensional terminological and logical/schema propositional interoperability.
• The necessity of comprehensive entity mappings and schema crosswalks for achieving semantic interoperability is highlighted.
• Additions to the FAIR Guiding Principles are suggested to address semantic interoperability requirements.

Conclusions:

• Attaining FAIR data necessitates robust semantic interoperability, supported by linguistic models and comprehensive mappings.
• A FAIR ecosystem of services, including terminology, schema, and operations services, is crucial for FAIR Digital Objects.
• Enhancing the FAIR Guiding Principles with specific semantic interoperability requirements will improve data discoverability and reusability.