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

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Publicado en:The Electronic Library vol. 39, no. 3 (2021), p. 469-485
Autor principal: Zhao, Xintong
Otros Autores: Greenberg, Jane, Meschke, Vanessa, Toberer, Eric, Hu, Xiaohua
Publicado:
Emerald Group Publishing Limited
Materias:
Big Data
Deep learning
Materials science
Ontology
Publications
Knowledge
Neural networks
Medical research
Web Ontology Language-OWL
Researchers
Academic publications
Unstructured data
Semantics
Extraction
Academic disciplines
Literary criticism
Research
Geographic Location
Indexing
Mineralogy
Journal Articles
Research Design
Natural Language Processing
Knowledge Level
Comparative Analysis
Language Processing
Organic Chemistry
Metallurgy
Cognitive Structures
Semiotics
Logical Thinking
Algorithms
Acceso en línea:Citation/Abstract
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Resumen: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.
ISSN:0264-0473
1758-616X
DOI:10.1108/EL-11-2020-0320
Fuente:Library Science Database