A Simple Standard for Sharing Ontological Mappings (SSSOM)

作者：Nicolas Matentzoglu, James P. Balhoff, Susan M. Bello, Chris Bizon, Matthew Brush, Tiffany J. Callahan, Christopher G. Chute, William D. Duncan, Chris T. Evelo, Davera Gabriel, John Graybeal, Alasdair Gray, Benjamin M. Gyori, Melissa Haendel, Henriette Harmse, Nomi L. Harris, Ian Harrow, Harshad Hegde, Amelia Hoyt, Charles Tapley Hoyt, Dazhi Jiao, Ernesto Jiménez-Ruiz, Simon Jupp, HyeongSik Kim, Sebastian Koehler, Thomas Liener, Qinqin Long, James Malone, J. A. McLaughlin, Julie A. McMurry, Sierra Moxon, Mónica Muñoz-Torres, David Osumi-Sutherland, James A. Overton, Bjoern Peters, Tim Putman, Núria Queralt-Rosiñach, Kent Shefchek, Harold R. Solbrig, Anne Thessen, Tania Tudorache, Nicole Vasilevsky, Alex H. Wagner, Chris Mungall · 发表于：Database · 年份：2022 · DOI：10.1093/database/baac035 · 被引用次数：1 · 研究领域：Biomedical Text Mining and Ontologies、Semantic Web and Ontologies、Research Data Management Practices

Despite progress in the development of standards for describing and exchanging scientific information, the lack of easy-to-use standards for mapping between different representations of the same or similar objects in different databases poses a major impediment to data integration and interoperability. Mappings often lack the metadata needed to be correctly interpreted and applied. For example, are two terms equivalent or merely related? Are they narrow or broad matches? Or are they associated in some other way? Such relationships between the mapped terms are often not documented, which leads to incorrect assumptions and makes them hard to use in scenarios that require a high degree of precision (such as diagnostics or risk prediction). Furthermore, the lack of descriptions of how mappings were done makes it hard to combine and reconcile mappings, particularly curated and automated ones. We have developed the Simple Standard for Sharing Ontological Mappings (SSSOM) which addresses these problems by: (i) Introducing a machine-readable and extensible vocabulary to describe metadata that makes imprecision, inaccuracy and incompleteness in mappings explicit. (ii) Defining an easy-to-use simple table-based format that can be integrated into existing data science pipelines without the need to parse or query ontologies, and that integrates seamlessly with Linked Data principles. (iii) Implementing open and community-driven collaborative workflows that are designed to evolve the stan...