About
In the last decades, we have experienced a substantial increase in the volume of published scientific articles and related research objects (e.g., data sets, software packages); a trend that is expected to continue. This opens up fundamental challenges including generating large-scale machine-readable representations of scientific knowledge, making scholarly data discoverable and accessible, and designing reliable, comprehensive, and equitable metrics to assess scientific impact. The main objective of Sci-K is to provide a forum for researchers and practitioners from different disciplines to present, educate, and guide research related to scientific knowledge. Specifically, we foresee three main themes that cover the most important challenges in this field: representation, discoverability, and assessment.
Representation
There is an urge for flexible, context-sensitive, fine-grained, and machine-actionable representations of scholarly knowledge that at the same time are structured, interlinked, and semantically rich. Scientific Knowledge Graphs (SKGs) are becoming ... increasingly popular as infrastructures for representing scholarly knowledge. They are large networks describing the actors (e.g., authors, organisations), documents (e.g., publications, patents), ancillary material (e.g., research data, software), contextual information (e.g., projects, fundings), and research knowledge (e.g., research topics, tasks, technologies) in this space as well as their reciprocal relationships. These resources provide substantial benefits to researchers, companies, and policymakers by powering several data-driven services for navigating, analysing, and making sense of research dynamics. Some SKGs examples include Microsoft Academic Graph (MAG), AMiner, Open Academic Graph, ScholarlyData.org, Semantic Scholar, PID Graph, Open Research Knowledge Graph, OpenCitations, and the OpenAIRE research graph. Regarding this aspect, the main challenge is related to the design of ontologies able to conceptualise scholarly knowledge, model its representation, and enable its exchange across different SKGs.
Discoverability
It is important that scholarly information is easily findable, discoverable, and visible, so that it can be mined and organised within SKGs. To this end, we need discovery tools able to crawl the Web and identify scholarly data, whether on a publisher’s ...website or elsewhere – institutional repositories, pre-print servers, open-access repositories, and others. This is a particularly challenging endeavour because it requires a deep understanding of both the scholarly communication landscape and the needs of a variety of stakeholders: researchers, publishers, funders, and the general public. Typically, in addition to the journal landing page, a paper's open version, pieces of software as well as data sets are often shared via alternative channels that are disconnected from the journal page. Currently, this is a major obstacle that the community of practitioners is facing for creating comprehensive knowledge graphs. In brief, the challenges are related to the discovery and extraction of entities and concepts, integration of information from heterogeneous sources, identification of duplicates, finding connections between entities, and identifying conceptual inconsistencies.
Assessment
Due to the continuous growth in the volume of research output, rigorous approaches for the assessment of research impact are now more valuable than ever. In this context, we urge reliable, comprehensive, and equitable metrics and indicators of the scientific impact ...and merit of publications, data sets, research institutions, individual researchers, and other relevant entities. Scientific impact refers to the attention a research work receives inside its respective and related disciplines, the social/mass media, etc. Scientific merit, on the other hand, relates to the quality aspects of a work, such as its novelty, reproducibility, FAIR-ness, and readability. Nowadays, due to the growing popularity of Open Science initiatives, a large number of useful science-related data sets have been made openly available, paving the way for the synthesis of more sophisticated indicators of scientific impact and merit and, consequently, more rigorous research assessment. For instance, in recent years, we observed a surge of large SKGs, which providevery rich and relatively clean sources of information about academics, their publications and relevant metadata. These SKGs can be used for the development of novel research assessment approaches.
