Towards a Knowledge Graph for Science

Abstract

The document-centric workflows in science have reached (or already exceeded) the limits of adequacy. This is emphasized by recent discussions on the increasing proliferation of scientific literature and the reproducibility crisis. This presents an opportunity to rethink the dominant paradigm of document-centric scholarly information communication and transform it into knowledgebased information flows by representing and expressing information through semantically rich, interlinked knowledge graphs. At the core of knowledge-based information flows is the creation and evolution of information models that establish a common understanding of information communicated between stakeholders as well as the integration of these technologies into the infrastructure and processes of search and information exchange in the research library of the future. By integrating these models into existing and new research infrastructure services, the information structures that are currently still implicit and deeply hidden in documents can be made explicit and directly usable. This has the potential to revolutionize scientific work as information and research results can be seamlessly interlinked with each other and better matched to complex information needs. Furthermore, research results become directly comparable and easier to reuse. As our main contribution, we propose the vision of a knowledge graph for science, present a possible infrastructure for such a knowledge graph as well as our early attempts towards an implementation of the infrastructure. * This invited article accompanies Sören Auer's WIMS2018 keynote.

SDM-RDFizer

“…Lastly, the activity of commits in the Github repository illustrates the attention paid to the creation of new versions and the resolution of the issues identified by the users of the tool. Predicted Impact: The number of visits of knowledge graphs like DBpedia and Wikidata, and the current developments in scientific (e.g., [1]) and industrial areas (e.g., [10]) evidence the need of providing efficient tools for knowledge graph management at scale. The experimental evaluations of SDM-RDFizer illustrate the benefits of grounding solutions for the problem of knowledge graph creation in the well-established areas of data integration systems and query processing.…”

“…Knowledge graphs have gained momentum as data structures to integrate-as factual statements-data and knowledge present in heterogeneous data sources. DBpedia and Wikidata are exemplary encyclopedic knowledge graphs frequently accessed by scientific and industrial communities; e.g., only Wikidata receives billions of visits per year 1 . Similarly, knowledge graphs are receiving significant attention in science and industrial developments [1,10].…”

“…DBpedia and Wikidata are exemplary encyclopedic knowledge graphs frequently accessed by scientific and industrial communities; e.g., only Wikidata receives billions of visits per year 1 . Similarly, knowledge graphs are receiving significant attention in science and industrial developments [1,10]. In fact, according to Google, knowledge graph is a trend term 2 and the Google Scholar indexes more than 3,5M entries of scientific publications with the term knowledge graph.…”

Mining Scholarly Publications for Scientific Knowledge Graph Construction

“…Their main limitation is that they typically represent the content of the papers as unstructured text (title, abstract, sometimes the full text). Therefore, a significant challenge in this field regards the generation of SKGs that contain also a explicit representation of the knowledge presented in the publications [2], and potentially describes entities such as approaches, application, formats, and so on.…”

Making Study Populations Visible Through Knowledge Graphs

“…We build a Study Cohort Ontology (SCO) (section 4) to support the vocabulary in these Table 1s (plural form) and to model their structure. Further, we encode Table 1s as Resource Description Framework (RDF) knowledge graphs (KGs) [3] (section 5) to expose in a declarative manner 3 these study populations. We demonstrate our ontology and the use of our knowledge graphs with two applications (section 6): one aimed at helping medical practitioners determine the similarity of a patient or a clinical population to the study population, and another aimed at supporting retrospective analysis of a study to expose possible biases or population gaps, such as racial underrepresentations.…”