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

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Achieving FAIR data requires specific semantic interoperability, focusing on meaning and reference. This involves linguistic models for data, entity mappings, and a FAIR ecosystem of services for reliable machine and human communication.

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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.