Overview of STEM Science as Process, Method, Material, and Data Named Entities

We are faced with an unprecedented production in scholarly publications worldwide. Stakeholders the digital libraries posit that document-based publishing paradigm has reached limits of adequacy. Instead, structured, machine-interpretable, fine-grained knowledge as Knowledge Graphs (KG) is strongly advocated. In this work, we develop and analyze a large-scale structured dataset STEM articles across 10 different disciplines, viz. Agriculture, Astronomy, Biology, Chemistry, Computer Science, Earth Engineering, Material Mathematics, Medicine. Our analysis defined over corpus comprising 60K abstracts four scientific entities process, method, material, data. Thus, our study presents, for first time, multidisciplinary under construct named entity labels specifically selected to be domain-independent opposed domain-specific. The work then inadvertently feasibility test characterizing science concepts. Further, summarize distinct facets per concept discipline, set word cloud visualizations offered. STEM-NER-60k corpus, created comprises 1 M extracted from 60k obtained major platform publicly released.