Open Science Graphs must interoperate!

Aryani A., Fenner M., Manghi P., Mannocci A., Stocker M.

Open Science  Research  Knowledge graphs  Interoperability 

Open Science Graphs (OSGs) are Scientific Knowledge Graphs whose intent is to improve the overall FAIRness of science, by enabling open access to graph representations of metadata about people, artefacts, institutions involved in the research lifecycle, as well as the relationships between these entities, in order to support stakeholder needs, such as discovery, reuse, reproducibility, statistics, trends, monitoring, impact, validation, and assessment. The represented information may span across entities such as research artefacts (e.g. publications, data, software, samples, instruments) and items of their content (e.g. statistical hypothesis tests reported in publications), research organisations, researchers, services, projects, and funders. OSGs include relationships between such entities and sometimes formalised (semantic) concepts characterising them, such as machine-readable concept descriptions for advanced discoverability, interoperability, and reuse. OSGs are generally valuable individually, but would greatly benefit from information exchange across their collections, thereby improving their efficacy to serve stakeholder needs. They could, therefore, reuse and exploit the data aggregation and added value that characterise each OSG, decentralising the effort and capitalising on synergies, as no one-size-fits-all solution exists. The RDA IG on Open Science Graphs for FAIR Data is investigating the motivation and challenges underpinning the realisation of an Interoperability Framework for OSGs. This work describes the key motivations for i) the definition of a classification for OSGs to compare their features, identify commonalities and differences, and added value and for ii) the definition of an Interoperability Framework, specifically an information model and APIs that enable a seamless exchange of information across graphs.

Source: International Workshops: DOING, MADEISD, SKG, BBIGAP, SIMPDA, AIMinScience 2020 and Doctoral Consortium, pp. 195–206, Lyon, France, 25-27/08/2020

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