Woondong Yeo

Aggregative and stochastic model of main path identification: a case study on graphene

This paper suggests a new method to search main path, as a knowledge trajectory, in the citation network. To enhance the performance and remedy the problems suggested by other researchers for main path analysis (Hummon and Doreian, Social Networks 11(1): 39–63, 1989), we applied two techniques, the aggregative approach and the stochastic approach. The first technique is used to offer improvement of link count methods, such as SPC, SPLC, SPNP, and NPPC, which have a potential problem of making a mistaken picture since they calculate link weights based on a individual topology of a citation link; the other technique, the second-order Markov chains, is used for path dependent search to improve the Hummon and Doreian’s priority first search method. The case study on graphene that tested the performance of our new method showed promising results, assuring us that our new method can be an improved alternative of main path analysis. Our method’s beneficial effects are summed up in eight aspects: (1) path dependent search, (2) basic research search rather than applied research, (3) path merge and split, (4) multiple main paths, (5) backward search for knowledge origin identification, (6) robustness for indiscriminately selected citations, (7) availability in an acyclic network, (8) completely automated search.

A quantitative approach to recommend promising technologies for SME innovation: a case study on knowledge arbitrage from LCD to solar cell

Small and medium-sized enterprises (SMEs) are more important today than in the past, due to their capabilities of creating jobs and boosting the economy. SMEs need continual innovation to survive in a competitive market and to continue growth. But SMEs suffer from the lack of information to generate innovative ideas. The objectives of this study are to suggest a new method to recommend promising technologies to SMEs that need “knowledge arbitrage” and to help SMEs come up with ideas on new R&D. To this end, this study used three analytic techniques: co-word analysis, collaborative filtering, and regression analysis. The suggested method is tested to assure its usefulness by the real case of knowledge arbitrage from LCD to Solar cell. The main contribution of this study is that it is the first to suggest the new method using recommendation algorithm (collaborative filtering) for SMEs’ knowledge arbitrage.

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.

Building Hierarchical Knowledge Base of Research Interests and Learning Topics for Social Computing Support

본 논문은 연구ㆍ학습 주제 지식베이스를 통한 소셜컴퓨팅 지원에 관한 연구로 두 가지 하부 연구로 구성되었다. 첫 번째 연구는 다양한 학문분야에서 전자 도서관 이용자들의 연구 및 학습 주제를 추출하기 위해 분야별로 분류가 잘 되어 있는 NDLTD Union catalog의 석박사 학위 논문 (Electronic Theses and Dissertations : ETDs)을 분석하여 계층적 지식베이스를 구축하는 연구이다. 석박사 학위 논문 이외에 ACM Transactions 저널의 논문과 컴퓨터 분야 국제 학술대회 웹사이트도 추가로 분석하였는데 이는 컴퓨팅 분야의 보다 세분화된 지식베이스를 얻기 위해서이다. 계층적 지식베이스는 개인화 서비스, 추천시스템, 텍스트 마이닝, 기술기회탐색, 정보 가시화 등의 정보서비스와 소셜컴퓨팅에 유용하게 사용될 수 있다. 본 논문의 두 번째 연구 부분에서는 우리가 만든 계층적 지식기반을 활용하여 4개의 사용자 커뮤니티 마이닝 알고리즘 중에서 우리가 수행중인 소셜 컴퓨팅 연구, 즉 구성원간의 결합도에 기반한 추천시스템,에 최상의 성능을 보이는 그룹핑 알고리즘을 찾는 성능 평가 연구 결과를 제시하였다. 우리는 이 논문을 통해서 우리가 제안하는 연구ㆍ학습 주제 데이터베이스를 사용하는 방법이 기존에 사용자 커뮤니티 마이닝을 위해 사용되던 비용이 많이 필요하고, 느리며, 개인정보 침해의 위험이 있는 인터뷰나 설문에 기반한 방법을 자동화되고, 비용이 적게 들고, 빠르고, 개인정보 침해 위험이 없으며, 반복 수행시에도 일관된 결과를 보여주는 방법으로 대체할 수 있음을 보이고자 한다.

Application of Research Paper Recommender System to Digital Library

AbstractThe progress of computers and Web has given rise to a rapid increase of the quantity of the useful information, which is making the demand of recommender systems widely expanding. Like in other domains, a recommender system in a digital library is important, but there are only a few studies about the recommender system of research papers, Moreover none is there in korea to our knowledge. In the paper, we seek for a way to develop the NDSL recommender system of research papers based on the survey of related studies. We conclude that NDSL needs to modify the way to collect users interests from explicit to implicit method, and to use user-based and memory-based collaborative filtering mixed with contents-based filtering(CF). We also suggest the method to mix two filterings and the use of personal ontology to improve user satisfaction. ■ keyword :∣Recommender System ∣Personalisation ∣Collaborative Filtering ∣Digital Library ∣ 접수번호 : #101014-002 접수일자 : 2010년 10월 14일 심사완료일 : 2010년 11월 25일교신저자 : 박영욱, e-mail : ywpark@kisti.re.kr

The implication of ANT (Actor-Network-Theory) methodology for R&D policy in open innovation paradigm

ABSTRACT Based on Actor-Network-Theory (ANT), this articles aims to analyse the origins and development of graphene R&D policies in Korea. At first, we have investigated the formation and variation of various actors through the application of the four steps of “translation” of ANT which is process of an actor aggregation: problematisation, interessement, enrolment, and mobilisation. Furthermore, we select three latent variables which represent the hybrid of networks, just like, media attention, government investment, and R&D achievements and look at the interaction of them with Partial Least Squares Structural Equation Modeling. In conclusion, this study presents a new research methodology that simulates ANT in connection with actual model construction and provides interesting implications that the media public sphere needs to be diversified and discussion of obstacles to rebel against the graphene network needs to be abundant.

Evaluating the Items Derived from Technology Foresight

Many organizations release future emerging technologies information because it is very important to companies. However, unfortunately there are few organizations who assess the emerging technologies they thought a few years ago. We made a framework for assessing the brightness of future emerging technologies rapidly and cost-effectively. We came up with 2 new concepts for it. One is product potential and the other is consumption potential. Product potential is relative probability that emerging technology is implemented to real products. It is resulted from analyzing patents related with emerging technology. Consumption potential is relative probability that consumers buy the products. The number of appearances of emerging technology in the mass media is related to consumption potential. We compared the brightness between LED and LCD technologies with proposed evaluating framework, and came to know that LCD has more brightness over LED.

Development of Informetric Model to Identify Emerging Technologies

Patent data have both properties of technological and industrial information. They satisfy explicit requirements for originality, technological validity, and commercial value. They comprise all fields of innovation for a long period of time. They show their own qualitative importance by forward citation of them. In this paper, we attempt to establish and apply an analytical model and process based on informetric approach using patent information in order to predict emerging technologies which have the possibility of industrial development in the future.