Katsumori Matsushima

Nanobiotechnology as an emerging research domain from nanotechnology: A bibliometric approach

Nanotechnology has been intensively investigated by bibliometric methods due to its technological importance and expected impacts on economic activity. However, there is less focus on nanobiotechnology, which is an emerging research domain in nanotechnology. In this paper, we study the current status of the former, with our primary focus being to reveal the structure and research domains in nanobiotechnology. We also examine country and institutional performance in nanobiotechnology. It emerged that nanostructures, drug delivery and biomedical applications, bio-imaging, and carbon nanotubes and biosensors are the major research domains, while the USA is the leading country, and China has also made substantial contribution. Most institutions having a major impact in the area of nanobiotechnology are located in the USA.

Topological analysis of citation networks to discover the future core articles

In this article, we investigated the factors determining the capability of academic articles to be cited in the future using a topological analysis of citation networks. The basic idea is that articles that will have many citations were in a “similar” position topologically in the past. To validate this hypothesis, we investigated the correlation between future times cited and three measures of centrality: clustering centrality, closeness centrality, and betweenness centrality. We also analyzed the effect of aging as well as of self‐correlation of times cited. Case studies were performed in the two following recent representative innovations: Gallium Nitride and Complex Networks. The results suggest that times cited is the main factor in explaining the near future times cited, and betweenness centrality is correlated with the distant future times cited. The effect of topological position on the capability to be cited is influenced by the migrating phenomenon in which the activated center of research shifts from an existing domain to a new emerging domain.

Computer‐assisted roadmapping: a case study in energy research

Science and technology (S&T) roadmaps are an attractive tool in R&D management, and have been widely used during the past decade. S&T roadmaps are typically constructed by gathering and stimulating experts opinion. In these days, the planner of S&T research has to grasp the broader coverage of scientific and technological research, and make decisions on effective investment in promising and emerging technologies especially under circumstances of limited resources. In such a situation, roadmapping is time-consuming and subjective, and therefore computer-based approach is expected to supplement expert-based approach. In this paper, we proposed and demonstrated that the computer-based approach using citation network analysis can be used to depict technology trend, and build the first draft of S&T roadmaps. We perform a case study in energy research. We track emerging research domains in it by citation network analysis. Our analysis confirms that the fuel cell and solar cell are rapidly growing domains in energy research. We further investigate the detailed research structures by clustering. Each citation cluster has characteristic research topics, and there is a variety of growth trends among the clusters. By using citation network analysis, we can track emerging research domains among a pile of publications efficiently and effectively.

Terminology-based knowledge mining for new knowledge discovery

In this article we present an integrated knowledge-mining system for the domain of biomedicine, in which automatic term recognition, term clustering, information retrieval, and visualization are combined. The primary objective of this system is to facilitate knowledge acquisition from documents and aid knowledge discovery through terminology-based similarity calculation and visualization of automatically structured knowledge. This system also supports the integration of different types of databases and simultaneous retrieval of different types of knowledge. In order to accelerate knowledge discovery, we also propose a visualization method for generating similarity-based knowledge maps. The method is based on real-time terminology-based knowledge clustering and categorization and allows users to observe real-time generated knowledge maps, graphically. Lastly, we discuss experiments using the GENIA corpus to assess the practicality and applicability of the system.

Enhanced co-citation analysis using frameworks

Abstract The valuation of intangible assets, such as patents, has recently been considered an important technique for R&D strategies. This paper proposes a citation analysis as a method of valuation on patent clusters having patent networks. A citation analysis is a method that examines the value of a patent on the basis of the number of citations of references. In particular, a co-citation analysis is used for coupling patents as the patent cluster and valuating the patent cluster. While the co-citation analysis is of great interest as a method of valuation, this analysis has not been fully examined and does not define any framework for a technology transition and patent strategy. Therefore, in this paper, the conventional co-citation analysis for evaluating the patent clusters is enhanced and frameworks for the technology transition and patent strategy are proposed by analysing citation routes in a co-citation analysis.

Detecting emerging research fronts in regenerative medicine by citation network analysis of scientific publications

In todays increasingly global and knowledge-based economy, competitiveness and growth depend on the ability of an economy to meet fast-changing market needs quickly and efficiently through management of new science and technology. Therefore, for both R&D managers and policy makers, noticing emerging research domains among numerous academic papers has become a significant task. However, such a task becomes highly laborious and difficult as each research domain becomes specialized and segmented. In this paper, we detect emerging research front from a huge number of academic papers related regenerative medicine, which is a case of radically innovative research. We divide citation networks into clusters using the topological clustering method, track the positions of papers in each cluster, and visualize citation networks with characteristic terms for each cluster. Analyzing the clustering results with the average age and parent-children relationship of each cluster could be helpful in detecting emergence. In addition, tracking topological measures, within-cluster degree z and participation coefficient P, enable us to determine whether there are emerging knowledge clusters. Our results show that our method succeeds to detect emerging research fronts in regenerative medicine and these results are confirmed as reasonable ones by experts.

Identifying informal communities and leaders for total quality management using network analysis of email

Global communication networks with internet and email have changed the nature of community and leadership communication style within an organization. For TQM-based organizational innovation, it is a key issue to identify communities of practice and deploy adequate leaders within the communities in the global communication. For effective TQM activities with leadership, we propose a method for indentifying informal communities and potential leaders from email data. In a clustering process, we have detected informal communities and hierarchical structures. Network centrality characterizes members of the informal communities within the structure of the email network. These measures contributed to identify leadership roles with the informal communities. According to the results of a case-study with actual email data of a firm, we discuss how our method can be applied for TQM-based organizational management that could lead to innovation.

Identifying the Large-Scale Structure of the Blogosphere

We analyze a topological structure of networks formed according to the entries and trackbacks in the blogosphere, which is a collection of weblog articles. The analysis is performed based on community extraction, network visualization and keyword analysis. It is shown that the large-scale structure of the blogosphere has a globally sparse, but locally dense structure. The entries in a community yield a dense structure while the trackbacks that interconnect communities are sparse. The visualized results show sparkling-firework-like patterns. We then attempt to characterize the communities using a tf-idf technique. It is found that specific topics are discussed in each community. These results will help us to identify the communities in which certain specific topics discussed and to detect trends in the blogosphere.

Email network analysis for organizational management

In this turbulent business environment of global recession, traditional organizational structure is reaching its limits. In order to accommodate itself to these changes, managing informal communication beyond old framework is indispensable. It is critical for innovation management to recognize communities of practice and informal leaders. In previous studies we have demonstrated our method was effective to indentify informal communities and potential leaders from one month email log data collected in September 2008 within an organization through a case study of a global manufacturing company. In this paper we collect the second set of one-month email log in June 2009 so as to chronologically compare with the first set of data collected in September 2008 and to analyze changes before and after major organizational changes triggered by the bankruptcy of Lehman Brothers. Email network analysis helps management systematically view its organization as a whole.

Early detection of innovations from citation networks

In this paper, we performed a comparative study in two research domains to develop a method of early detection of seeds of innovations. We divided the papers in each research domain into clusters using the topological clustering method, tracked the evolution of the clusters and the positions of the papers in each cluster, and visualized citation networks with cluster name for each cluster. And we also investigated the correlation between future times cited and three measures of centrality, i.e., clustering centrality, closeness centrality, betweenness centrality, the effect of aging and of self-correlation of times cited. With these analyses, we proposed how to distinguish incremental and radical innovations, to detect emerging papers which could be seeds of radical innovations, and to predict the capability of academic papers to be cited in the future.