Strategy for Extensible, Evolving Terminology for the Materials Genome Initiative Efforts
- 538 Downloads
Intuitive, flexible, and evolving terminology plays a significant role in capitalizing on recommended knowledge representation models for materials engineering applications. In this article, we present a proposed rules-based approach with initial examples from a growing corpus of materials terms in the National Institute of Standards and Technology (NIST) Materials Data Repository. Our method aims to establish a common, consistent, and evolving set of rules for creating or extending terminology as needed to describe materials data. The rules are intended to be simple and generalizable for users to understand and extend as well as for groups to apply to their own repositories. The rules generate terms that facilitate machine processing and decision making.
- 2.ASTM International, Computerization and Networking of Materials Databases. ASTM International ASTM Special Technical Publication: 1017, 1106, 1140, 1257, 1311, vol. 1–5 (West Conshohocken: ASTM International, 1989–1997).Google Scholar
- 5.M. Yamazaki, Y. Xu, M. Murata, H. Tanaka, K. Kamihira, and K. Kimura, Baltica VII: Life Management and Maintenance for Power Plants, Vol. 2, ed. P. Auerkari and J. Hal (Espo: Valtion Teknillinen Tutkimuskeskus, 2007), pp. 193–207.Google Scholar
- 9.J. Rumble, S. Freiman, and C. Teague, Uniform Description System for Materials on the Nanoscale, CODATA/VAMAS Joint Working Group on the Description of Nanomaterials (2014), http://www.codata.org/uploads/Uniform_Description_System_Nanomaterials-Published-v01-15-02-01.pdf.
- 10.National Science and Technology Council, “Materials Genome Initiative for Global Competitiveness” (Executive Office of the President, Washington, DC, 2011), http://www.whitehouse.gov/sites/default/files/microsites/ostp/materials_genome_initiative-final.pdf.
- 11.J.P. Holdren, “Increasing access to the results of federally funded scientific research” (Memorandum for the heads of executive departments and agencies, Office of Science and Technology Policy, Executive Office of the President, Washington, DC, 2013), http://www.whitehouse.gov/sites/default/files/microsites/ostp/ostp_public_access_memo_2013.pdf.
- 12.G. Kaufman and E.F. Begley, Adv. Mater. Process. 161(11), 35 (2003).Google Scholar
- 16.K. Cheung, J. Drennan, and J. Hunter, AAAI Spring Symposium: Semantic Scientific Knowledge Integration, eds. D.L. McGuinness, P. Fox, and B. Brodaric (Palo Alto: AAAI, 2008), pp. 9–14.Google Scholar
- 17.M. Rubacha, A.K. Rattan, and S.C. Hosselet, JALA 16, 90 (2011).Google Scholar
- 22.T.N. Bhat, JSWIS 6, 22 (2010).Google Scholar
- 26.M. Smith, M. Barton, M. Bass, M. Branschofsky, G. McClellan, D. Stuve, R. Tansley, and J.H. Walker, D-Lib. Mag. 9 (1), (2003). http://hdl.handle.net/1721.1/29465.
- 28.A. Rauber, and S. Pröll, “Scalable Dynamic Data Citation Approaches, Reference Architectures and Applications, RDA WG Data Citation Position Paper,” http://www.rd-alliance.org/groups/data-citation-wg/wiki/scalable-dynamic-data-citation-rda-wg-dc-position-paper.html.