“Scientific Knowledge Graphs provide the means for a structured representation of information and thus facilitate Open Science by connecting scholarly artefacts. The represented information range from research artefacts (e.g. publications, data, software, samples, instruments) and items of their content, research organisations, researchers, services, projects, funders and more. Currently Knowledge Graphs are implemented as Research Infrastructures addressing, each of them comprises information from various sources, connecting and enriching these information. The federation of Knowledge Graphs would provide various benefits serving the development of an integrated RI ecosystem.”
“Metadata as Knowledge,” is a special issue of KULA: Knowledge Creation, Dissemination, and Preservation Studies that takes up the critical relationship between metadata and knowledge. The issue includes articles and project reports that address metadata, hidden knowledge, and labour; standards versus expression; knowledge sharing and reuse of metadata; forays into open and shared knowledge; linked data, metadata translation, and discovery; and machine learning and knowledge graphs. Although rarely an object of notice or scrutiny by its users, metadata governs the circulation of information and has the power to name, broadcast, normalize, oppress, and exclude. As the contributions to this issue demonstrate, metadata is knowledge, and metadata creators, systems, and practices must contend with how metadata means.
(Source: Editors’ introduction – Allison-Cassin, Stacy, and Dean Seeman. 2022. Metadata as Knowledge. KULA: Knowledge Creation, Dissemination, and Preservation Studies 6(3). https://doi.org/10.18357/kula.244 )
Abstract: Current science communication has a number of drawbacks and bottlenecks which have been subject of discussion lately: Among others, the rising number of published articles makes it nearly impossible to get a full overview of the state of the art in a certain field, or reproducibility is hampered by fixed-length, document-based publications which normally cannot cover all details of a research work. Recently, several initiatives have proposed knowledge graphs (KG) for organising scientific information as a solution to many of the current issues. The focus of these proposals is, however, usually restricted to very specific use cases. In this paper, we aim to transcend this limited perspective and present a comprehensive analysis of requirements for an Open Research Knowledge Graph (ORKG) by (a) collecting and reviewing daily core tasks of a scientist, (b) establishing their consequential requirements for a KG-based system, (c) identifying overlaps and specificities, and their coverage in current solutions. As a result, we map necessary and desirable requirements for successful KG-based science communication, derive implications, and outline possible solutions.