Journal article
An exploratory analysis: extracting materials science knowledge from unstructured scholarly data
Electronic library, v 39(3), pp 469-485
04 Nov 2021
Featured in Collection : UN Sustainable Development Goals @ Drexel
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 newts 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.
Metrics
Details
- Title
- An exploratory analysis: extracting materials science knowledge from unstructured scholarly data
- Creators
- Xintong Zhao - Drexel Univ, Metadata Res Ctr, Dept Informat Sci, Coll Comp & Informat, Philadelphia, PA 19104 USAJane Greenberg - Drexel Univ, Metadata Res Ctr, Dept Informat Sci, Coll Comp & Informat, Philadelphia, PA 19104 USAVanessa Meschke - Colorado Sch Mines, Dept Phys, Golden, CO 80401 USAEric Toberer - Colorado Sch Mines, Dept Phys, Golden, CO 80401 USAXiaohua Hu - Drexel University
- Publication Details
- Electronic library, v 39(3), pp 469-485
- Publisher
- Emerald Group Publishing
- Number of pages
- 17
- Grant note
- 1940239; 1940199 / US National Science Foundation, Office of Advanced Cyberinfrastructure (NSF/OAC); National Science Foundation (NSF)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000685050200001
- Scopus ID
- 2-s2.0-85112196772
- Other Identifier
- 991019168297004721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Information Science & Library Science