Hyuck Jai Lee

Emerging technologies: quantitative identification and measurement

Emerging technologies present both challenges and opportunities for national technology strategies. National governments may therefore want to monitor the technological horizon on a systematic basis. This article outlines the quantitative approaches available for such monitoring. Among the standard types of bibliometric data, proposals and publications are most likely to be useful for this purpose since they capture information earlier in the cycle of technology development. Patents, in contrast, trail behind. Analysis can proceed with keywords or citations, and algorithms are available to use the information structure inherent in these kinds of data to identify and measure emerging areas. There are limitations, however, in all the available approaches and the authors therefore recommend using them in conjunction with expert methods by focusing the qualitative assessment in particular areas.

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.

Research Trend Visualization by MeSH Terms from PubMed

Motivation: PubMed is a primary source of biomedical information comprising search tool function and the biomedical literature from MEDLINE which is the US National Library of Medicine premier bibliographic database, life science journals and online books. Complimentary tools to PubMed have been developed to help the users search for literature and acquire knowledge. However, these tools are insufficient to overcome the difficulties of the users due to the proliferation of biomedical literature. A new method is needed for searching the knowledge in biomedical field. Methods: A new method is proposed in this study for visualizing the recent research trends based on the retrieved documents corresponding to a search query given by the user. The Medical Subject Headings (MeSH) are used as the primary analytical element. MeSH terms are extracted from the literature and the correlations between them are calculated. A MeSH network, called MeSH Net, is generated as the final result based on the Pathfinder Network algorithm. Results: A case study for the verification of proposed method was carried out on a research area defined by the search query (immunotherapy and cancer and “tumor microenvironment”). The MeSH Net generated by the method is in good agreement with the actual research activities in the research area (immunotherapy). Conclusion: A prototype application generating MeSH Net was developed. The application, which could be used as a “guide map for travelers”, allows the users to quickly and easily acquire the knowledge of research trends. Combination of PubMed and MeSH Net is expected to be an effective complementary system for the researchers in biomedical field experiencing difficulties with search and information analysis.

Development of a Practical Tool for Exploring the Map of Technology

This study suggests a way to utilize the map of technology as a guide to find new technology component. Recent studies of mapping knowledge mainly focused on analyzing the map as a result of technological innovation rather than utilizing the map for exploring the world of technological innovation. The preliminary result of a case study suggests that a firm can find possible technology components that can be combined with own technology component. The map of technology comprises the nodes of International Patent Classification (IPC) main groups and the links presenting the co-assign relationship between the IPC main groups.

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.