Abstract: The aeronautical and automotive industries have, as an essential objective, the energy efficiency optimization of aircraft and cars, while maintaining stringent functional requirements. One working line focuses on the use of lightweight structural materials to replace conventional materials. For this reason, it is considered enlightening to carry out an analysis of the literature published over the last 20 years through Open Access literature. For this purpose, a systematic methodology is applied to minimize the possible risks of bias in literature selection and analysis. Web of Science is used as a search engine. The final selection comprises the 30 articles with the highest average numbers of citations per year published from 2015 to 2020 and the 7 articles published from the period of 2000–2014. Overall, the selection is composed of 37 Open Access articles with 2482 total citations and an average of 67.1 citations per article/year published, and includes Q1 (62%) and Q2 (8%) articles and proceeding papers (30%). The study seeks to inform about the current trends in materials and processes in lightweight structural materials for aeronautical and automotive applications with a sustainable perspective. All the information collected is summarized in tables to facilitate searches and interpretation by interested researchers. View Full-Text
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.
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.
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.
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.