An exploratory analysis: extracting materials science knowledge from unstructured scholarly data
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| Argitaratua izan da: | The Electronic Library vol. 39, no. 3 (2021), p. 469-485 |
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| Egile nagusia: | |
| Beste egile batzuk: | , , , |
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Emerald Group Publishing Limited
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| Sarrera elektronikoa: | Citation/Abstract Full Text Full Text - PDF |
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MARC
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| 001 | 2634110433 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 0264-0473 | ||
| 022 | |a 1758-616X | ||
| 024 | 7 | |a 10.1108/EL-11-2020-0320 |2 doi | |
| 035 | |a 2634110433 | ||
| 045 | 2 | |b d20210501 |b d20210630 | |
| 084 | |a 36136 |2 nlm | ||
| 100 | 1 | |a Zhao, Xintong |u Department of Information Science, Metadata Research Center, Drexel University, Philadelphia, Pennsylvania, USA | |
| 245 | 1 | |a An exploratory analysis: extracting materials science knowledge from unstructured scholarly data | |
| 260 | |b Emerald Group Publishing Limited |c 2021 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Big Data | ||
| 653 | |a Deep learning | ||
| 653 | |a Materials science | ||
| 653 | |a Ontology | ||
| 653 | |a Publications | ||
| 653 | |a Knowledge | ||
| 653 | |a Neural networks | ||
| 653 | |a Medical research | ||
| 653 | |a Web Ontology Language-OWL | ||
| 653 | |a Researchers | ||
| 653 | |a Academic publications | ||
| 653 | |a Unstructured data | ||
| 653 | |a Semantics | ||
| 653 | |a Extraction | ||
| 653 | |a Academic disciplines | ||
| 653 | |a Literary criticism | ||
| 653 | |a Research | ||
| 653 | |a Geographic Location | ||
| 653 | |a Indexing | ||
| 653 | |a Mineralogy | ||
| 653 | |a Journal Articles | ||
| 653 | |a Research Design | ||
| 653 | |a Natural Language Processing | ||
| 653 | |a Knowledge Level | ||
| 653 | |a Comparative Analysis | ||
| 653 | |a Language Processing | ||
| 653 | |a Organic Chemistry | ||
| 653 | |a Metallurgy | ||
| 653 | |a Cognitive Structures | ||
| 653 | |a Semiotics | ||
| 653 | |a Logical Thinking | ||
| 653 | |a Algorithms | ||
| 700 | 1 | |a Greenberg, Jane |u Department of Information Science, Metadata Research Center, Drexel University, Philadelphia, Pennsylvania, USA | |
| 700 | 1 | |a Meschke, Vanessa |u Department of Physics, Colorado School of Mines, Golden, Colorado, USA | |
| 700 | 1 | |a Toberer, Eric |u Department of Physics, Colorado School of Mines, Golden, Colorado, USA | |
| 700 | 1 | |a Hu, Xiaohua |u Department of Information Science, Metadata Research Center, Drexel University, Philadelphia, Pennsylvania, USA | |
| 773 | 0 | |t The Electronic Library |g vol. 39, no. 3 (2021), p. 469-485 | |
| 786 | 0 | |d ProQuest |t Library Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2634110433/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/2634110433/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/2634110433/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |