Yeong-Ho Moon

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.

Longitudinal Study-Based Dementia Prediction for Public Health

The issue of public health in Korea has attracted significant attention given the aging of the country’s population, which has created many types of social problems. The approach proposed in this article aims to address dementia, one of the most significant symptoms of aging and a public health care issue in Korea. The Korean National Health Insurance Service Senior Cohort Database contains personal medical data of every citizen in Korea. There are many different medical history patterns between individuals with dementia and normal controls. The approach used in this study involved examination of personal medical history features from personal disease history, sociodemographic data, and personal health examinations to develop a prediction model. The prediction model used a support-vector machine learning technique to perform a 10-fold cross-validation analysis. The experimental results demonstrated promising performance (80.9% F-measure). The proposed approach supported the significant influence of personal medical history features during an optimal observation period. It is anticipated that a biomedical “big data”-based disease prediction model may assist the diagnosis of any disease more correctly.

Understanding the Dynamic Convergence Phenomenon from the Perspective of Diversity and Persistence: A Cross-Sector Comparative Analysis between the United States and South Korea.

This study was designed to improve the explanation for the behavior of the phenomenon of technology convergence. The concepts and measurements of diversity and persistence, as inherent attributes of the phenomenon, were elaborated by reviewing different theories. Diversity was examined by analyzing the degree of capability to absorb heterogeneous technologies, while persistence was investigated by analyzing the degree of continuity in the usage of cumulated technologies. With these two dimensions, an analytic framework was proposed to compare the differences and dynamic patterns of convergence competence by countries at the technology sector level. Three major technology sectors in the United States and South Korea, namely, information and communication technology, biotechnology, and nanotechnology, were explored to explicitly illustrate the differences in technology convergence competence. The results show that although Korea has narrowed the differences of capabilities for technology convergence compared to the US, Korea not only has to continuously pursue the improvement of specialization for all three sectors, but also has to encourage the exploitation of different technology fields. The suggested framework and indicators allow for monitoring of the dynamic patterns of a technology sector and identifying the sources of the gaps. Thus, the framework and indicators are able to ensure the purpose of government innovation policy and to provide strategic directions for redistributing the proper combination of sources to accomplish technology convergence.

A Comparative Study on the Trend of Technological Convergence

In recent years, `technological fusion or convergence` has drawn a lot of attention of innovation researchers and governmental policy makers as the driving force of technological innovation and industrial growth. There are, however, few studies on the analysis of longitudinal trends of technological convergence and its comparison between global and national level. In this study, with the citation data of about 18 million articles, we analyzed 1) the growth of representative convergence research areas, 2) the convergence of citing patterns between research fields, and 3) the changing trend of diversity index of all research fields. We conclude that technological convergence in korea shows the relatively strong orientation to the combination of neighboring fields than that of heterogenous fields in comparison to global trend. In particular, the relatively weak activity of cognitive science and the low level of mutual exchange between arts/humanities/social sciences and natural/engineering sciences in Korea are emphasized.

RFM analysis for detecting future core technology

RFM is a simple and powerful method to provide a framework for understanding and quantifying customer behavior based on purchase in marketing field. The purpose of this study is to demonstrate that RFM analysis can be effectively used for predicting future core technologies. Experimental results obtained using the US patent data show that recency, frequency, and monetary are efficient variables to identify the future core patents. In addition, the rules to identify the future core technology are searched using the classification and regression tree (CART), combined with the two sampling methods (over- and under-sampling) and the learning algorithms are compared in terms of precision, recall, and F-measure. Computational studies demonstrate that over-sampling method is effective for finding rules from imbalanced data, such as the data for detecting future core 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.