مقاله انگلیسی رایگان در مورد برنامه ریزی فناوری با کلمه کلیدی فنی مبتنی تحلیل داده
|عنوان مقاله||Monitoring emerging technologies for technology planning using technical keyword based analysis from patent data|
|ترجمه عنوان مقاله||نظارت بر فناوری های در حال ظهور برای برنامه ریزی فناوری با استفاده از کلمه کلیدی فنی مبتنی بر تجزیه و تحلیل داده های دارای حق انحصاری|
|تعداد صفحات مقاله||۱۲ صفحه|
|رشته های مرتبط||مدیریت|
|گرایش های مرتبط||مدیریت تکنولوژی و مدیریت فناوری اطلاعات|
|مجله||پیش بینی فنی و تغییر اجتماعی – Technological Forecasting & Social Change|
|دانشگاه||گروه مهندسی صنایع و مدیریت، دانشگاه علم و صنعت، کره|
|کلمات کلیدی||تجزیه و تحلیل ثبت اختراع، مدل مبتنی بر کلید واژه، کلید واژه فنی، برنامه ریزی فناوری|
|تعداد کلمات||۶۴۳۵ کلمه|
|لینک مقاله در سایت مرجع||لینک این مقاله در سایت الزویر (ساینس دایرکت) Sciencedirect – Elsevier|
|وضعیت ترجمه مقاله||ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.|
|دانلود رایگان مقاله||دانلود رایگان مقاله انگلیسی|
|سفارش ترجمه این مقاله||سفارش ترجمه این مقاله|
|بخشی از متن مقاله:|
Monitoring of emerging technologies can identify incipient technological changes quickly, and is an invaluable component of technology planning, and of development of research and development (R&D) policy by governments and companies (Ashton et al., 1991). By investing in R&D strategically in potentially-important emerging technologies, companies can become market winners or early followers of market leaders (Hamilton, 1985). Given that technology monitoring can help companies’ development of new products, technologies or joint ventures, monitoring of emerging technologies provides a starting point to induce radical technological change or mergers and acquisitions (M&A) (Ashton et al., 1994). Furthermore, many methods in technology forecasting predict emerging technologies, but have limited ability to identify possible emerging technologies. Therefore, use of reliable sources (e.g., research organization reports) to comprehend emerging technologies allows examination of how they are specifically realized. To constantly monitor emergence of technologies, patents are the best source of technology information, because they contain technical details. Patent analysis has been considered as a basis for technology assessment to monitor sources of technological knowledge (Ernst, 2003). Patent documents describe commercialized inventions, and the number of granted patents represents a company’s rate of technological advance (Ernst, 2001). For these reasons, analysis of patent data has two major benefits. First, patents are written to protect the right of invention and to prevent overlapping R&D investment, so they are reliable formal sources of unique technical information. Second, patent database systems are well-organized and are supplied with retrieval systems, so large numbers of patents can be examined easily. These systems present up-to-date information, and can rapidly check new applied or granted patents and their applications; moreover, patent information is available to everyone. For example, since 1976, the United States Patent and Trademark Office (USPTO) has provided full-text patents and a retrieval system that can be used free of charge by anyone. For these reasons, stakeholders such as R&D policy makers, R&D managers, technology developers, and R&D planners have used patent information to support identification of world-wide technical evolution (Zhang, 2011; Altuntas et al., 2015b), and to support making in R&D (Thorleuchter et al., 2010; Altuntas et al., 2015a). As a result, users can examine technological trends in which competitors’ R&D strategies, technological resources, and external knowledge of technology are embedded.
Many techniques have been used in patent information analysis to monitor technological trends. Engelsman and van Raan (1992) suggested co-word maps to identify declining or emerging fields of technological activities, which were identified by examining keywords at the meso-level and micro-level. Yoon et al. (2002) proposed patent maps that displayed patents in two or three-dimensional space according to similarity of keywords; an absence of patents in the map was considered as a starting point to identify emerging technologies. Yoon and Park (2004) developed keyword-based patent network and identified up-to-date trends of high technologies by using network analysis. In addition, the k-Means algorithm (Kim et al., 2008), a formal concept analysis-based approach (Lee et al., 2011), and a novelty detection technique (Geum et al., 2013) have been used in keyword-patent matrices to capture technological flows and emerging patterns. More-advanced techniques have been described, such as an approach based on subject-action-object (SAO) relationships. Choi et al. (2011) suggested using an SAO-based network to identify technology trends; the authors constructing a noun-by-verb relationship matrix, so emerging technologies could be inferred by low density and high cohesion in the network. Wang et al. (2015) conducted SAO-based technology roadmapping to comprehend developing trends.
However, previous research to monitor technological trends has some limitations in the process of keyword selection and in identification of relatedness of keywords that all keyword-based patent analysis methods share. These processes rely too much on the intervention of experts, so the reliability of the analysis can be greatly affected by the experts’ opinions. Expert-dependent analysis is also expensive because it is timeconsuming and laborious. Keyword selection is the most crucial factor, but the main keywords are chosen based on the subjective judgment of experts (Tseng et al., 2007; Noh et al., 2015). Although some research (Yoon and Park, 2004; Lee et al., 2009; Geum et al., 2013) suggested term frequency to guide experts in evaluating significant terms, experts must still take time to eliminate common words. Moreover, previous extraction of keywords could not effectively analyze multiple-phrase words even if they articulated a patent document well. Identification of the relatedness of keywords as synonyms, hypernyms, and hyponyms raises the quality of semantic processing when comparing patent documents, but assessment of their relatedness has been completely dependent on technical experts. Choi et al. (2011) and Wang et al. (2015) proposed using a word ontology such asWordNet (Miller, 1995) to understand the relatedness of keywords, but WordNet uses only a hierarchy of generic terms, so it does not consider technical terms; therefore, it does not produce a good solution because patents include many technical terms.