Bangrae Lee

Mapping Korea's national R&D domain of robot technology by using the co-word analysis

In this paper, we show a “Strategic Diagram” of the robot technology by applying the co-word analysis to the metadata of Korean related national R&D projects in 2001. The strategic diagram shows the evolutionary trends of the specific R&D domain and relational patterns between subdomains. We may use this strategic diagram to support both the strategic planning and the R&D Program.

Identification of dependency patterns in research collaboration environments through cluster analysis

In this paper, we present a new way of detecting dependency patterns in research collaboration environments. We use co-authorship data at the organization level to measure the degree of research collaboration. Thus we adopt a special clustering technique, called ‘cross-associations clustering’, to extract the dependency patterns among research groups. To assist in evaluating the dependency patterns, we suggest a collaboration dependency index to indicate whether a research group is dependent on other groups. In our work, as target research environments, we choose four significant areas: alternative energy, water shortage, food shortage and global warming. Through extensive cluster analysis, we have found that dependency patterns exist in the areas of alternative energy, water shortage and global warming, but not in the food shortage area.

Automated Detection of Influential Patents Using Singular Values

Centrality measures such as degree centrality have been utilized to identify influential and important patents in a citation network. However, no existing centrality measures take into consideration information from the change of the similarity matrix. This paper presents a new centrality measure based on the change of a node similarity matrix. The proposed approach gives more intuitive understanding of the finding of the influential nodes. The present study starts off with the assumption that the change of matrix that may result from removing a given node would assess the importance of the node since each node make a contribution to the given similarity matrix between nodes. The various matrix norms using the singular values such as nuclear norm which is the sum of all singular values, are used for calculating the contribution of a given node to a node similarity matrix. In other words, we can obtain the change of matrix norms for a given node after we calculate the singular values for the case of the nonexistence and the case of existence of the node. Then, the node resulting in the largest change (i.e., decrease) of matrix norms can be considered as the most important node. Computation of singular values can be computationally intensive when the similarity matrix size is large. Therefore, the singular value update technique is also developed for the case of the network with large nodes. We compare the performance of our proposed approach with other widely used centrality measures using U.S. patents data in the area of information and security. Experimental results show that our proposed approach is competitive or even performs better compared to existing approaches.

Development of the KnowledgeMatrix as an Informetric Analysis System

Application areas of Knowledge Discovery in Database(KDD) have been expanded to many R&D management processes including technology trends analysis, forecasting and evaluation etc. Established research field such as informetrics (or scientometrics) has utilized techniques or methods of KDD. Various systems have been developed to support works of analyzing large-scale R&D related databases such as patent DB or bibliographic DB by a few researchers or institutions. But extant systems have some problems for korean users to use. Their prices is not moderate, korean language processing is impossible, and user`s demands not reflected. To solve these problems, Korea Institute of Science and Technology Information(KISTI) developed stand-alone type information analysis system named as KnowledgeMatrix. KnowledgeMatrix system offer various functions to analyze retrieved data set from databases. KnowledgeMatrix`s main operation unit is composed of user-defined lists and matrix generation, cluster analysis, visualization, data pre-processing. Matrix generation unit help extract information items which will be analyzed, and calculate occurrence, co-occurrence, proximity of the items. Cluster analysis unit enable matrix data to be clustered by hierarchical or non-hierarchical clustering methods and present tree-type structure of clustered data. Visualization unit offer various methods such as chart, FDP, strategic diagram and PFNet. Data pre-processing unit consists of data import editor, string editor, thesaurus editor, grouping method, field-refining methods and sub-dataset generation methods. KnowledgeMatrix show better performances and offer more various functions than extant systems.

Morphological Classification of Knowledge Map for Science and Technology and Development of Knowledge Map Examples in the View of Information Analysis

Knowledge maps for science and technology are used extensively in the research projects. However, they are not organized systematically and are not necessarily suitable to be used in the research projects. Therefore, this study aims to organize the knowledge maps in order to support scientific research projects. To this end, the existing knowledge maps for science and technology are classified as one of four types based on data representation methods; the frequency summary map, trend summary map, distribution-based knowledge map and network-based knowledge map. Additionally, by summarizing and classifying the knowledge maps through the principle of `five w`s and one h`, the unexplored area are investigated. Finally, some examples of useful knowledge maps in terms of data analysis are provided with details such as definitions, components and utilization purposes. These findings may be a starting point for future research into a better understanding of knowledge maps for science and technology.

A Graph Kernel Approach for Detecting Core Patents and Patent Groups

In todays business environment, competition within industries is becoming more and more intense. To survive in this fast-paced competitive environment, its important to know what the core patents are and how the patents can be grouped. This study focuses on discovering core patents and clustering patents using a patent citation network in which core patents are represented as an influential node and patent groups as a cluster of nodes. Existing methods have discovered influential nodes and cluster nodes separately, especially in a citation network. This study develops a method used to detect influential nodes (that is, core patents) and clusters (that is, patent groups) in a patent citation network simultaneously rather than separately. The method allows a core patent in each patent group to be discovered easily and the distribution of similar patents around a core patent to be recognized. For this study, kernel k-means clustering with a graph kernel is introduced. A graph kernel helps to compute implicit similarities between patents in a high-dimensional feature space.

A Study on the Regional Speciality of the S&T Outcomes in Korea

The advance of science and technology becomes the nerves of the development of economy and industry of our future in the regional level as well as in the national and international level. In Korea, it has been more than 10 years since local governments launched, and they are strategically fostering their specialized regional industries. Both the central government and the regional governments prepare and execute policies to foster specialized regional industries. Though there are many kinds of methods to analyze the outcomes of science and technology of region, in this paper, we measure the outcomes of science and technology of region by applying an informetric analysis on the SCIE papers and USPA patents. To seek for the regional speciality, we analyze the total national outcomes and the regional outcomes of S&T activities in Korea.