Achieving data interoperability is fundamental for successful FAIR (Findable, Accessible, Interoperable, and Reusable) data initiatives, and the utilization of ontology and terminology standards plays a crucial role in this endeavor. Nevertheless, challenges arise when organizations adopt competing or overlapping standards, necessitating access to mapping solutions to align datasets effectively. This issue is especially prevalent in industries, where internal and private terminology codes must be mapped to public ontology identifiers for integrating public datasets seamlessly.
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High-quality mapping between public ontologies exists, and standards have emerged that facilitate the publication and sharing of these mappings. Numerous tools and best practices are also available to generate novel ontology
mappings. Despite such progress, industries continue to invest significant resources into generating and curating mappings. Consequently, services are in demand to ensure mappings remain updated and accurate as data and
ontologies grow and evolve.
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