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Tech Mining for Emerging STI Trends Through Dynamic Term Clustering and Semantic Analysis: The Case of Photonics
Tech mining (TM) helps to acquire intelligence about the evolution of research and development (R&D), technologies, products, and markets for various STI areas and what is likely to emerge in the future by identifying trends. The present chapter introduces a methodology for the identification of trends through a combination of “thematic clustering” based on the co-occurrence of terms, and “dynamic term clustering” based on the correlation of their dynamics across time. In this way, it is possible to identify and distinguish four patterns in the evolution of terms, which eventually lead to (i) weak signals of future trends, as well as (ii) emerging, (iii) maturing, and (iv) declining trends. Key trends identified are then further analyzed by looking at the semantic connections between terms identified through TM. This helps to understand the context and further features of the trend. The proposed approach is demonstrated in the field photonics as an emerging technology with a number of potential application areas.
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Technology foresight has been increasingly undertaken by developing countries to identify technologies whose adoption might serve as a platform for future economic growth. However, foresight activities have not, by and large, resulted in well-developed policy initiatives. Three factors are relevant for improvement. First, foresight activities would benefit from being more informed by the convergence literature and global convergence experience over the past several decades, and should therefore incorporate organically the concepts of absorptive capacity and technology gap into foresight exercises. Second, certain preconditions – in particular the existence of a functional national innovation system – enhance the likelihood that foresight exercises will be successful. Third, in order to achieve wide buy-in and promote the sustainability of initiatives generated by the foresight activity, developing countries are advised to consult widely in the foresight process. Policies emanating from foresight activities should additionally address two core challenges: a) a clear definition of those technologies that should be developed internally vs. those that should be sourced from abroad and b) identification of the internal capabilities to be developed in conjunction with those technologies targeted for acquisition from abroad.
This is a textbook in data analysis. Its contents are heavily influenced by the idea that data analysis should help in enhancing and augmenting knowledge of the domain as represented by the concepts and statements of relation between them. According to this view, two main pathways for data analysis are summarization, for developing and augmenting concepts, and correlation, for enhancing and establishing relations. Visualization, in this context, is a way of presenting results in a cognitively comfortable way. The term summarization is understood quite broadly here to embrace not only simple summaries like totals and means, but also more complex summaries such as the principal components of a set of features or cluster structures in a set of entities.
The material presented in this perspective makes a unique mix of subjects from the fields of statistical data analysis, data mining, and computational intelligence, which follow different systems of presentation.
Science, technology and innovation (STI) involves numerous policy fields which are championed by different government ministries or agencies. A consistent and coherent anticipatory policy mix is understood to be one that ensures a timely development and implementation of various forward-looking policy instruments. Such timely implementation is crucial for the eventual impact of the policy measures. This also requires that foresight for STI policies looks beyond the potential development paths and challenges but includes the time dimension and the outline of necessary policy responses including a relevant implementation framework. In addition the institutions which are part of the National Innovation Systems (NIS) should to be considered thoroughly for a well-balanced and comprehensive policy mix. Not only national but also regional and local actors need to be involved—and they need to be involved not only in the implementation of policy but at much earlier stages in the foresight and subsequent design procedures of the policy mix. One practical approach for convincing and engaging NIS actors at different levels is to stress opportunities which offer advantages to each of them, instead of just focusing on challenges and problems.
Работа отражает новые результаты, полученные в ходе совместного проекта кафедры математической лингвистики СПбГУ с разработчиками Национального корпуса русского языка (НКРЯ, http://ruscorpora.ru). Цель исследования – разработка технологии автоматического распознавания в тексте конструкций, связанных с той или иной лексической единицей, и применение этой технологии в создании каталога русских лексических конструкций. Выделение конструкций предполагает использование потенциала многоплановой лингвистической разметки НКРЯ (прежде всего, лексико-семантической). В докладе обсуждается использование модуля визуализации данных для уточнения информации о конструкциях, реализующих значения исследуемых слов. Модуль предназначен для лексикографов и исследователей лексики и грамматики русского языка.
A vast amount of documents in the Web have duplicates, which is a challenge for developing efficient methods that would compute clusters of similar documents. In this paper we use an approach based on computing (closed) sets of attributes having large support (large extent) as clusters of similar documents. The method is tested in a series of computer experiments on large public collections of web documents and compared to other established methods and software, such as biclustering, on same datasets. Practical efficiency of different algorithms for computing frequent closed sets of attributes is compared.
Foresight has gained much attention as a tool for developing and informing science, technology and innovation policy and company strategies. It is frequently used for detecting not only potential development paths of technologies but also possible economic and societal changes; and for identifying challenges that nations, societies and companies might face in the future. Raising awareness within the respective communities of trends and challenges is critically important—and the biggest challenge is how we can develop measures to meet these anticipated challenges. Paradoxically, perhaps, it may be more helpful for creating and implementing successful measures if these are elaborated by thinking about grasping opportunities, rather than framing them in terms of threats that have to be responded to. Accordingly there is a need to change the mindsets in science, technology and innovation policy making—and to engender solution and opportunity orientation among scientists and engineers.
Tech Mining, a special form of “Big Data” analytics, aims to generate Competitive Technical Intelligence (CTI) using bibliometric and text-mining software (e.g., VantagePoint, TDA) as well as other analytical & visualization applications for analyses of Science, Technology & Innovation (ST&I) information resources. The goal of the conference is to ENGAGE cross-disciplinary networks of analysts, software specialists, researchers, policymakers, and managers toADVANCE the use of textual information in multiple science, technology, and business development fields. The conference program will address key CHALLENGES in:
Data
Sourcing, preparing, and interpreting data sources including patents, publications, webscraping, and other novel data sourcesText-mining tools and methods
Best practices in software-based topic modeling, clumping, association rules, term manipulation, text manipulation, etc. VisualizationApplied research
Future-Oriented Technology Analysis (FTA) Intelligence gathering to support decision-making in the private sector (e.g., Management of Technology)This conference is intended for researchers and students across multiple fields, especially Scientometrics, Public Policy, Management of Technology and Information Science.