Requirement-oriented core technological components’ identification based on SAO analysis

Chao Yang (), Donghua Zhu (), Xuefeng Wang (), Yi Zhang (), Guangquan Zhang () and Jie Lu
Additional contact information
Chao Yang: Beijing Institute of Technology
Donghua Zhu: Beijing Institute of Technology
Xuefeng Wang: Beijing Institute of Technology
Yi Zhang: Beijing Institute of Technology
Guangquan Zhang: University of Technology Sydney

Scientometrics, 2017, vol. 112, issue 3, No 4, 1229-1248

Abstract: Abstract Technologies play an important role in the survival and development of enterprises. Understanding and monitoring the core technological components (e.g., technology process, operation method, function) of a technology is an important issue for researchers to develop R&D policy and manage product competitiveness. However, it is difficult to identify core technological components from a mass of terms, and we may experience some difficulties with describing complete technical details and understanding the terms-based results. This paper proposes a Subject-Action-Object (SAO)-based method, in which (1) a syntax-based approach is constructed to extract the SAO structures describing the function, relationship and operation in specified topics; (2) a systematic method is built to extract and screen technological components from SAOs; and (3) we propose a “relevance indicator” to calculate the relevance of the technological components to requirements, and finally identify core technological components based on this indicator. Based on the considerations for requirements and novelty, the core technological components identified have great market potential and can be useful in monitoring and forecasting new technologies. An empirical study of graphene is performed to demonstrate the proposed method. The resulting knowledge may hold interest for R&D management and corporate technology strategies in practice.

Keywords: Subject-Action-Object (SAO); Patent analysis; Text mining; Technological components identification (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
http://link.springer.com/10.1007/s11192-017-2444-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:scient:v:112:y:2017:i:3:d:10.1007_s11192-017-2444-5

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11192

DOI: 10.1007/s11192-017-2444-5

Access Statistics for this article

Scientometrics is currently edited by Wolfgang Glänzel

More articles in Scientometrics from Springer, Akadémiai Kiadó
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().