caa a22 4500 445348631 CHVBK 20180317142837.0 cr unu---uuuuu 170323e20110701xx s 000 0 eng 10.1007/s11192-011-0383-0 doi (NATIONALLICENCE)springer-10.1007/s11192-011-0383-0 Identifying rapidly evolving technological trends for R&D planning using SAO-based semantic patent networks [Elektronische Daten] [Janghyeok Yoon, Kwangsoo Kim] Patents constitute an up-to-date source of competitive intelligence in technological development; thus, patent analysis has been a vital tool for identifying technological trends. Patent citation analysis is easy to use, but fundamentally has two main limitations: (1) new patents tend to be less cited than old ones and may miss citations to contemporary patents; (2) citation-based analysis cannot be used for patents in databases which do not require citations. Naturally, citation-based analysis tends to underestimate the importance of new patents and may not work in rapidly-evolving industries in which technology life-cycles are shortening and new inventions are increasingly patented world-wide. As a remedy, this paper proposes a patent network based on semantic patent analysis using subject-action-object (SAO) structures. SAO structures represent the explicit relationships among components used in a patent, and are considered to represent key concepts of the patent or the expertise of the inventor. Based on the internal similarities between patents, the patent network provides the up-to-date status of a given technology. Furthermore, this paper suggests new indices to identify the technological importance of patents, the characteristics of patent clusters, and the technological capabilities of competitors. The proposed method is illustrated using patents related to synthesis of carbon nanotubes. We expect that the proposed procedure and analysis will be incorporated into technology planning processes to assist experts such as researchers and R&D policy makers in rapidly-evolving industries. Akadémiai Kiadó, Budapest, Hungary, 2011 Patent mining nationallicence Semantic patent similarity nationallicence Subject-action-object (SAO) structures nationallicence Patent network nationallicence Research and development (R&D) trend nationallicence Natural language processing (NLP) nationallicence Yoon Janghyeok Department of Industrial and Management Engineering, Pohang University of Science and Technology, San 31, Hyoja-dong, Nam-gu, 790-784, Pohang, Kyungbuk, Republic of Korea aut Kim Kwangsoo Department of Industrial and Management Engineering, Pohang University of Science and Technology, San 31, Hyoja-dong, Nam-gu, 790-784, Pohang, Kyungbuk, Republic of Korea aut Scientometrics Springer Netherlands 88/1(2011-07-01), 213-228 0138-9130 88:1<213 2011 88 11192 https://doi.org/10.1007/s11192-011-0383-0 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11192-011-0383-0 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Yoon Janghyeok Department of Industrial and Management Engineering, Pohang University of Science and Technology, San 31, Hyoja-dong, Nam-gu, 790-784, Pohang, Kyungbuk, Republic of Korea aut NATIONALLICENCE 700 1- Kim Kwangsoo Department of Industrial and Management Engineering, Pohang University of Science and Technology, San 31, Hyoja-dong, Nam-gu, 790-784, Pohang, Kyungbuk, Republic of Korea aut NATIONALLICENCE 773 0- Scientometrics Springer Netherlands 88/1(2011-07-01), 213-228 0138-9130 88:1<213 2011 88 11192 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer