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

Xintong Zhao (Department of Information Science, Metadata Research Center, Drexel University, Philadelphia, Pennsylvania, USA)
Jane Greenberg (Department of Information Science, Metadata Research Center, Drexel University, Philadelphia, Pennsylvania, USA)
Vanessa Meschke (Department of Physics, Colorado School of Mines, Golden, Colorado, USA)
Eric Toberer (Department of Physics, Colorado School of Mines, Golden, Colorado, USA)
Xiaohua Hu (Department of Information Science, Metadata Research Center, Drexel University, Philadelphia, Pennsylvania, USA)

The Electronic Library

ISSN: 0264-0473

Article publication date: 9 August 2021

Issue publication date: 4 November 2021

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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.

Keywords

Acknowledgements

The research reported on in this paper is supported, in part, by the US National Science Foundation, Office of Advanced Cyberinfrastructure (NSF/OAC: #1940239 and #1940199). The authors also acknowledge the support of Cyra Gallano and Evan Dubrunfaut, Drexel University, for their role as data evaluators.

Citation

Zhao, X., Greenberg, J., Meschke, V., Toberer, E. and Hu, X. (2021), "An exploratory analysis: extracting materials science knowledge from unstructured scholarly data", The Electronic Library, Vol. 39 No. 3, pp. 469-485. https://doi.org/10.1108/EL-11-2020-0320

Publisher

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Emerald Publishing Limited

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