Abstract:

Biodiversity research has a strong focus on the links between environment and functional traits, e.g., to assess how anthropogenic drivers of change impact ecological systems (Díaz et al. 2013). Interoperable exchange and integration of such data is enabled through the use of ontologies that provide ”meaning” to data and enable downstream processing involving learning and inference over graph-structured models of these data (Kulmanov et al. 2020). However, the development of thematically similar semantic artifacts, e.g., the Environmental Ontology (ENVO, Buttigieg et al. 2016) and the Semantic Web for Earth and Environment Technology Ontology (SWEET, DiGiuseppe et al. 2014), in biodiversity-related disciplines (e.g., environmental genomics and earth observation) can introduce substantial conceptual overlaps, and highlights the need for bridging technologies to facilitate reuse of biodiversity data across those knowledge fields (Karam et al. 2020). A recent design study, funded by the European Open Science Cloud (EOSC), proposes a framework to create, document and publish mappings and crosswalks linking different semantic artifacts within a particular scientific community and across scientific domains under the label of "Flexible Semantic Mapping Framework" (SEMAF, Broeder et al. 2021). SEMAF puts a strong emphasis on so-called pragmatic mappings, i.e., mappings that are driven by specific interoperability goals such as translations between specific observation measurements (e.g., sensor configurations) and metadata descriptions. Within the Horizon Europe Project “Biodiversity Digital Twin for Advanced Modelling, Simulation and Prediction Capabilities" (BioDT), a mapping tool leveraging SEMAF is currently under development: Mapping.bio provides a lightweight web service to read semantic artifacts, visualize them, add mappings as graphical connections and store the mappings as FAIR (Findable, Accessible, Interoperable Reusable) Digital Objects (FDOs, De Smedt et al. 2020) in a repository. To foster reusability, sustainably and long-term availability of digital objects, mapping.bio features mappings compliant with the Simple Standard for Sharing Ontological Mappings (SSSOM, Matentzoglu et al. 2022), a machine-interpretable and extensible vocabulary enabling the self-contained exploration and processing of annotated mappings by machines (machine actionability, Jacobsen et al. 2020).

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