An exploratory analysis: extracting materials science knowledge from unstructured scholarly data

Abstract

Purpose The output of academic literature has increased significantly due to digital technology, presenting researchers with a challenge across every discipline, including materials science, as it is impossible to manually read and extract knowledge from millions of published literature. The purpose of this study is to address this challenge by exploring knowledge extraction in materials science, as applied to digital scholarship. An overriding goal is to help inform readers about the status knowledge extraction in materials science. Design/methodology/approach The authors conducted a two-part analysis, comparing knowledge extraction methods applied materials science scholarship, across a sample of 22 articles; followed by a comparison of HIVE-4-MAT, an ontology-based knowledge extraction and MatScholar, a named entity recognition (NER) application. This paper covers contextual background, and a review of three tiers of knowledge extraction (ontology-based, NER and relation extraction), followed by the research goals and approach. Findings The results indicate three key needs for researchers to consider for advancing knowledge extraction: the need for materials science focused corpora; the need for researchers to define the scope of the research being pursued, and the need to understand the tradeoffs among different knowledge extraction methods. This paper also points to future material science research potential with relation extraction and increased availability of ontologies. Originality/value To the best of the authors’ knowledge, there are very few studies examining knowledge extraction in materials science. This work makes an important contribution to this underexplored research area.

Framework for entity extraction with verification: application to inference of data set usage in research publications

“…A computational procedure to identify emerging topics in periodical materials was proposed and tested in the context of the gallery, library, archive and museum institutions (Candela and Carrasco, 2021). Text-based entity extraction and information retrieval from publications were also applied in engineering (a mining project) (Tomašević et al , 2018), materials science (Zhao et al , 2021) and in the nuclear domain using 120 documents retrieved from the Scopus database (Desul et al , 2019). Techniques were proposed for extracting tables and figures (Clark and Diwala, 2015; Wei et al , 2006) and free text descriptors of images (Lin et al , 2008).…”

“…This formulation is very general and can be applied to many real-world scenarios. For example, extracting sentiment information from tweets about companies, politicians or sports teams involves named entity recognition (NER) as a first step and has been studied extensively EL 40,4 (Nadeau and Sekine, 2007;Ratinov and Roth, 2009) in a variety of domains ranging from biomedical information (Perera et al, 2015;Zhao et al, 2021) to tweets (Li et al, 2012). In such scenarios, problems arise from the noisy nature of the data (e.g.…”