A Simple Standard for Sharing Ontological Mappings (SSSOM)

Abstract

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 standard continuously to address changing requirements and mapping practices. (iv) Providing reference tools and software libraries for working with the standard. In this paper, we present the SSSOM standard, describe several use cases in detail and survey some of the existing work on standardizing the exchange of mappings, with the goal of making mappings Findable, Accessible, Interoperable and Reusable (FAIR). The SSSOM specification can be found at http://w3id.org/sssom/spec. Database URL: http://w3id.org/sssom/spec

Unifying the Identification of Biomedical Entities with the Bioregistry

“…In addition to the native JSON format, the Bioregistry data is made available as a set of YAML and TSV files to facilitate reuse. Further, equivalence mappings between resources in external registries are exported into the Simple Standard for Sharing Ontological Mappings (SSSOM) 26 format. SSSOM is a standard for sharing mappings between different namespaces that we use to represent mappings between resources appearing in different registries, such as the relations exemplified in Figure 1C.…”

“…These support the the interoperability of data annotations and the conversion between URIs and CURIEs in Semantic Web applications. The Simple Standard for Sharing Ontological Mappings (SSSOM) 36 is a metadata standard for various mappings (e.g., equivalences) between ontology and database terms and an associated toolset (https://github.com/mapping-commons/sssom-py) based on LinkML (https://linkml.io) for loading, validating, and converting SSSOM content. The standard is meant to encourage higher quality curation in biomedical ontologies which often lack important metadata such as the mapping type, a standardized prefix and local unique identifier for the subject and object terms, provenance about how the mapping was generated, and provenance about who generated the mapping.…”

“…The Bioregistry has already been integrated with several projects aimed at data integration, knowledge assembly, and semantically annotated publications, described in section 3 on use cases and integrations. Current integrations of Bioregistry include BridgeDb 22 , PheKnowLator 23 , Manubot 24 , Biomappings 25 , SSSOM 26 , INDRA 27 , and the OBO Foundry 19 .…”

“…Overview statistics of the Bioregistry version 0.5.36 (2022-07-07). PheKnowLator 23 , Manubot24 , Biomappings 25 , SSSOM26 , INDRA27 , and the OBO Foundry19 .…”

Food process ontology requirements

“…If a term resource is not completely OBO compatible, data translation could still be accomplished by term mapping using the "has database cross reference" (oboInOwl:hasDbXref) data property, or via SSSOM mapping [30] for finer semantic tuning that can differentiate between semantic broad/narrow/exact synonymy.…”