Toward Representing Research Contributions in Scholarly Knowledge Graphs Using Knowledge Graph Cells
There is currently a gap between the natural language expression of scholarly publications and their structured semantic content modeling to enable intelligent content search. With the volume of research growing exponentially every year, a search feature operating over semantically structured content is compelling. Toward this end, in this work, we propose a novel semantic data model for modeling the contribution of scientific investigations. Our model, i.e. the Research Contribution Model (RCM), includes a schema of pertinent concepts highlighting six core information units, viz. Objective, Method, Activity, Agent, Material, and Result, on which the contribution hinges. It comprises bottom-up design considerations made from three scientific domains, viz. Medicine, Computer Science, and Agriculture, which we highlight as case studies. For its implementation in a knowledge graph application we introduce the idea of building blocks called Knowledge Graph Cells (KGC), which provide the following characteristics: (1) they limit the expressibility of ontologies to what is relevant in a knowledge graph regarding specific concepts on the theme of research contributions; (2) they are expressible via ABox and TBox expressions; (3) they enforce a certain level of data consistency by ensuring that a uniform modeling scheme is followed through rules and input controls; (4) they organize the knowledge graph into named graphs; (5) they provide information for the front end for displaying the knowledge graph in a human-readable form such as HTML pages; and (6) they can be seamlessly integrated into any existing publishing process thatsupports form-based input abstracting its semantic technicalities including RDF semantification from the user. Thus RCM joins the trend of existing work toward enhanced digitalization of scholarly publication enabled by an RDF semantification as a knowledge graph fostering the evolution of the scholarly publications beyond written text.