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

TL;DR

This paper introduces a large-scale, multidisciplinary dataset of STEM article abstracts annotated with four domain-independent scientific entities, enabling analysis and visualization of scientific knowledge across disciplines.

Contribution

It presents the creation and analysis of the STEM-NER-60k dataset, a large, publicly available corpus with structured annotations for four scientific entity types across ten STEM fields.

Findings

First large-scale analysis of multidisciplinary scientific entities

Domain-independent entity labels effectively characterize diverse STEM fields

Word cloud visualizations summarize key facets of scientific knowledge

Abstract

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