Donghua Zhu

Automated extraction and visualization of information for technological intelligence and forecasting

Abstract Empirical technology forecasting (TF) is not well utilized in technology management. Three factors could enhance managerial utilization: capability to exploit huge volumes of available information, ways to do so very quickly, and informative representations that help manage emerging technologies. This paper reports on efforts to address these three factors via partially automated processes to generate helpful knowledge from text quickly and graphically. We first illustrate a process to generate a family of technology maps that help convey emphases, players, and patterns in the development of a target technology. Second, we exemplify the generation of particular “innovation indicators” that measure particular facets of R&D activity to relate these to technological maturation, contextual influences, and market potential. Both technology mapping and innovation indicators rely upon searches in huge, easily accessible, abstract databases and text mining software. We augment these through “macros” (programming scripts) that automatically sequence the necessary steps to generate particular desired information products. These analytical findings can be tailored to the needs of particular technology managers.

A process for mining science & technology documents databases, illustrated for the case of "knowledge discovery and data mining"

This paper presents a process of mining research & development abstract databases to profile current status and to project potential developments for target technologies, The process is called “technology opportunities analysis.” This article steps through the process using a sample data set of abstracts from the INSPEC database on the topic o “knowledge discovery and data mining.” The paper offers a set of specific indicators suitable for mining such databases to understand innovation prospects. In illustrating the uses of such indicators, it offers some insights into the status of knowledge discovery research.

A hybrid visualisation model for technology roadmapping: bibliometrics, qualitative methodology and empirical study

Technology roadmapping offers a flexible instrument to portray development status in support of technology forecasting and assessment. This paper integrates bibliometrics with qualitative methodologies and visualisation techniques to construct a hybrid model for composing technology roadmaps. The mapping arrays details on the evolution of the technology under study and contributes to understanding the macro-technology development status. We generate a global technology roadmap for electric vehicles to demonstrate the approach in an empirical study.

A patent analysis method to trace technology evolutionary pathways

Increased competition due to rapid technological development pushes all participants in the market to focus on the prospect of New and Emerging Science & Technologies (NESTs). One promising NEST, dye-sensitized solar cells (DSSCs), has attracted attention in recent years. We focus on three research questions: how can we estimate DSSCs research activity trends; how can we identify DSSCs market expansion patterns; and, seeking to identify potential subsystems, what are the likely evolutionary paths of DSSCs development? In this paper, patent analysis is applied to help determine the developmental stage of a particular technology and trace its potential evolutionary pathways. In addition, since patent information can reflect commercial degree, we use patent transfer patterns to help evaluate market shift prospects.

Collaboration network and pattern analysis: case study of dye-sensitized solar cells

Nowadays, the development of emerging technology has become a double-edged sword in the scientific world. It can not only bring lots of innovation to society, but may also cause some terrible consequences due to its unknown factors. International collaboration may be able to reduce risks, which means a lot to the exploration of the emerging technology. Taking dye-sensitized solar cells (DSSCs) as an example, this paper examines the rapid growth of Chinese DSSCs research and the rise of collaboration between China and other countries/region. We use bibliometric and social network analysis methods to explore the patterns of scientific collaboration at country, institution and individual levels using data from the Science Citation Index. Examining overall trends shows that China has increased her position in DSSCs around the world. Furthermore, by focusing on the individual level, we find that the most influential authors tend to have fixed co-author networks and author name order, which is something worth considering. We use co-author analysis software independently developed to check three kinds of fixed co-author networks to explore author contributions, influence, and Author Activity Index rank in collaboration networks and use the rank we calculated to further explain author contributions in the networks. Results show that Chinese-X (e.g., Chinese-American) authors have pushed the collaboration between country and country and almost every kind of small network has a top author in it to gather others together. The modified author activity index rank list may reflect real research level. Author collaboration patterns have been impacted by the kinds of their institutions to some degree. These results can undoubtedly promote the international collaboration and the innovation process in the similar emerging technology fields.

Scientific evolutionary pathways: Identifying and visualizing relationships for scientific topics

Whereas traditional science maps emphasize citation statistics and static relationships, this paper presents a term‐based method to identify and visualize the evolutionary pathways of scientific topics in a series of time slices. First, we create a data preprocessing model for accurate term cleaning, consolidating, and clustering. Then we construct a simulated data streaming function and introduce a learning process to train a relationship identification function to adapt to changing environments in real time, where relationships of topic evolution, fusion, death, and novelty are identified. The main result of the method is a map of scientific evolutionary pathways. The visual routines provide a way to indicate the interactions among scientific subjects and a version in a series of time slices helps further illustrate such evolutionary pathways in detail. The detailed outline offers sufficient statistical information to delve into scientific topics and routines and then helps address meaningful insights with the assistance of expert knowledge. This empirical study focuses on scientific proposals granted by the United States National Science Foundation, and demonstrates the feasibility and reliability. Our method could be widely applied to a range of science, technology, and innovation policy research, and offer insight into the evolutionary pathways of scientific activities.

A hybrid similarity measure method for patent portfolio analysis

Similarity measures are fundamental tools for identifying relationships within or across patent portfolios. Many bibliometric indicators are used to determine similarity measures; for example, bibliographic coupling, citation and co-citation, and co-word distribution. This paper aims to construct a hybrid similarity measure method based on multiple indicators to analyze patent portfolios. Two models are proposed: categorical similarity and semantic similarity. The categorical similarity model emphasizes international patent classifications (IPCs), while the semantic similarity model emphasizes textual elements. We introduce fuzzy set routines to translate the rough technical (sub-) categories of IPCs into defined numeric values, and we calculate the categorical similarities between patent portfolios using membership grade vectors. In parallel, we identify and highlight core terms in a 3-level tree structure and compute the semantic similarities by comparing the tree-based structures. A weighting model is designed to consider: 1) the bias that exists between the categorical and semantic similarities, and 2) the weighting or integrating strategy for a hybrid method. A case study to measure the technological similarities between selected firms in China’s medical device industry is used to demonstrate the reliability our method, and the results indicate the practical meaning of our method in a broad range of informetric applications.

A patent time series processing component for technology intelligence by trend identification functionality

Abstract Technology intelligence indicates the concept and applications that transform data hidden in patents or scientific literatures into technical insight for technology strategy-making support. The existing frameworks and applications of technology intelligence mainly focus on obtaining text-based knowledge with text mining components. However, what is the corresponding technological trend of the knowledge over time is seldom taken into consideration. In order to capture the hidden trend turning points and improve the framework of existing technology intelligence, this paper proposes a patent time series processing component with trend identification functionality. We use piecewise linear representation method to generate and quantify the trend of patent publication activities, then utilize the outcome to identify trend turning points and provide trend tags to the existing text mining component, thus making it possible to combine the text-based and time-based knowledge together to support technology strategy making more satisfactorily. A case study using Australia patents (year 1983–2012) in Information and Communications Technology industry is presented to demonstrate the feasibility of the component when dealing with real-world tasks. The result shows that the new component identifies the trend reasonably well, at the same time learns valuable trend turning points in historical patent time series.

Analyzing collaboration networks and developmental patterns of nano-enabled drug delivery (NEDD) for brain cancer

Summary The rapid development of new and emerging science & technologies (NESTs) brings unprecedented challenges, but also opportunities. In this paper, we use bibliometric and social network analyses, at country, institution, and individual levels, to explore the patterns of scientific networking for a key nano area – nano-enabled drug delivery (NEDD). NEDD has successfully been used clinically to modulate drug release and to target particular diseased tissues. The data for this research come from a global compilation of research publication information on NEDD directed at brain cancer. We derive a family of indicators that address multiple facets of research collaboration and knowledge transfer patterns. Results show that: (1) international cooperation is increasing, but networking characteristics change over time; (2) highly productive institutions also lead in influence, as measured by citation to their work, with American institutes leading; (3) research collaboration is dominated by local relationships, with interesting information available from authorship patterns that go well beyond journal impact factors. Results offer useful technical intelligence to help researchers identify potential collaborators and to help inform R&D management and science & innovation policy for such nanotechnologies.

China's patterns of international technological collaboration 1976–2010: a patent analysis study

This study focuses on the trajectories and patterns of Chinas international collaborations over the period 1976–2010, using patent statistics and association analysis methods. The results identify those government policies that have significantly encouraged changes in the scale and scope of Chinas collaborations since 1997. The USA and Taiwan are major international collaborators with China, and the top cooperative entities are large-scale multi-national firms, which specialise in production, sales research and development of information or electronics technology; in contrast, universities and research institutions have a negligible presence in international collaborative patenting. This study finds that although China has developed significant international collaborative networks since the 1990s, it still needs to extend these ties to an even greater range of international partners and establish a broader scope of research interests.