Semantic Distances for Technology Landscape Visualization

This paper presents a novel approach to the visualization and subsequent elucidation of research domains in science and technology. The proposed methodology is based on the use of bibliometrics; i.e., analysis is conducted using information regarding trends and patterns of publication rather than the contents of these publications. In particular, we explore the use of term co-occurence frequencies as an indicator of the semantic closeness between pairs of words or phrases. To demonstrate the utility of this approach, a case study on renewable energy technologies is conducted, where the above techniques are used to visualize the interrelationships within a collection of energy-related keywords. As these are regarded as manifestations of the underlying research topics, we contend that the proposed visualizations can be interpreted as representations of the underlying technology landscape. These techniques have many potential applications, but one interesting challenge in which we are particularly interested is the mapping and subsequent prediction of future developments in the technological fields being studied.The research described in this paper was funded by the Masdar Institute of Science and Technology (MIST)

Tech mining: a revisit and navigation

This mini-review arrays the pertinent tools and purposes of “Tech Mining” – shorthand for empirical analyses of Science, Technology and Innovation (ST&I) data. The intent is to introduce the range of tools, and show how they can complement each other. Tech Mining aims to generate powerful intelligence to help manage R&D and innovation processes. We offer a 5-part array to help relate the analytical elements. An overview of a case study of Hybrid and Electric Vehicles illustrates the complexities involved and the potential to generate valuable “intel.

Characterizing the potential of being emerging generic technologies: A Bi-Layer Network Analytics-based Prediction Method

© 2019 17th International Conference on Scientometrics and Informetrics, ISSI 2019 - Proceedings. All rights reserved. Despite tremendous involvement of bibliometrics in profiling technological landscapes and identifying emerging topics, how to predict potential technological change is still unclear. This paper proposes a bi-layer network analytics-based prediction method to characterize the potential of being emerging generic technologies. Initially, based on the innovation literature, three technological characteristics are defined, and quantified by topological indicators in network analytics; a link prediction approach is applied for reconstructing the network with weighted missing links, and such reconstruction will also result in the change of related technological characteristics; the comparison between the two ranking lists of terms can help identify potential emerging generic technologies. A case study on predicting emerging generic technologies in information science demonstrates the feasibility and reliability of the proposed method

Discovering and forecasting interactions in big data research: A learning-enhanced bibliometric study

© 2018 As one of the most impactful emerging technologies, big data analytics and its related applications are powering the development of information technologies and are significantly shaping thinking and behavior in today's interconnected world. Exploring the technological evolution of big data research is an effective way to enhance technology management and create value for research and development strategies for both government and industry. This paper uses a learning-enhanced bibliometric study to discover interactions in big data research by detecting and visualizing its evolutionary pathways. Concentrating on a set of 5840 articles derived from Web of Science covering the period between 2000 and 2015, text mining and bibliometric techniques are combined to profile the hotspots in big data research and its core constituents. A learning process is used to enhance the ability to identify the interactive relationships between topics in sequential time slices, revealing technological evolution and death. The outputs include a landscape of interactions within big data research from 2000 to 2015 with a detailed map of the evolutionary pathways of specific technologies. Empirical insights for related studies in science policy, innovation management, and entrepreneurship are also provided

An entropy-based indicator system for measuring the potential of patents in technological innovation: rejecting moderation

© 2017, Akadémiai Kiadó, Budapest, Hungary. How to evaluate the value of a patent in technological innovation quantitatively and systematically challenges bibliometrics. Traditional indicator systems and weighting approaches mostly lead to “moderation” results; that is, patents ranked to a top list can have only good-looking values on all indicators rather than distinctive performances in certain individual indicators. Orienting patents authorized by the United States Patent and Trademark Office (USPTO), this paper constructs an entropy-based indicator system to measure their potential in technological innovation. Shannon’s entropy is introduced to quantitatively weight indicators and a collaborative filtering technique is used to iteratively remove negative patents. What remains is a small set of positive patents with potential in technological innovation as the output. A case study with 28,509 USPTO-authorized patents with Chinese assignees, covering the period from 1976 to 2014, demonstrates the feasibility and reliability of this method

Detecting and predicting the topic change of Knowledge-based Systems: A topic-based bibliometric analysis from 1991 to 2016

© 2017 The journal Knowledge-based Systems (KnoSys) has been published for over 25 years, during which time its main foci have been extended to a broad range of studies in computer science and artificial intelligence. Answering the questions: “What is the KnoSys community interested in?” and “How does such interest change over time?” are important to both the editorial board and audience of KnoSys. This paper conducts a topic-based bibliometric study to detect and predict the topic changes of KnoSys from 1991 to 2016. A Latent Dirichlet Allocation model is used to profile the hotspots of KnoSys and predict possible future trends from a probabilistic perspective. A model of scientific evolutionary pathways applies a learning-based process to detect the topic changes of KnoSys in sequential time slices. Six main research areas of KnoSys are identified, i.e., expert systems, machine learning, data mining, decision making, optimization, and fuzzy, and the results also indicate that the interest of KnoSys communities in the area of computational intelligence is raised, and the ability to construct practical systems through knowledge use and accurate prediction models is highly emphasized. Such empirical insights can be used as a guide for KnoSys submissions

Advanced Materials and Technologies in Nanogenerators

This reprint discusses the various applications, new materials, and evolution in the field of nanogenerators. This lays the foundation for the popularization of their broad applications in energy science, environmental protection, wearable electronics, self-powered sensors, medical science, robotics, and artificial intelligence