• Journal of Pharmacopuncture
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• v.6 no.3
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• pp.87-90
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• 2003

The purpose of this study is to investigate and analyze the selection process of the Science Citation Index(SCI). SCI has been the benchmarking medium of scientific journals for over 40 years by reviewing thousands of journals published each year. The discipline of Traditional Oriental Medicine has been gaining global interest in recent years, yet the number of related journals indexed by SCI has been minimal. Korean Medicine has been producing number of quality journals for decades, but lack of journals in the English language and other problems has hindered consideration for registering with SCI. This paper will focus on the journal selection process at ISI and recommend the criteria needed for a journal to be considered for SCI selection.

• The KIPS Transactions:PartD
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• v.19D no.1
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• pp.15-20
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• 2012

In this paper, to solve a vested interests of old papers in scientific literature ranking, we propose novel method that considers not only the current citations from other published papers but also the latent citations of papers to be published in the future. Furthermore, the method also considers the relevance of contents in the citing and cited papers. Finally, we verify the superiority of our proposed method through extensive experiments.

• Journal of the Korea Society of Computer and Information
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• v.21 no.11
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• pp.85-91
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• 2016

This paper proposes a collective approach to cited text recognition by exploiting a set of citing text from different articles citing the same article. First, the proposed method gathers highly-ranked cited sentences from the cited article using a group of citing text to create a collective information of probable cited sentences. Then, such collective information is used to determine final cited sentences among highly-ranked sentences from similarity-based cited text recognition. Experiments have been conducted on the data set which consists of research articles from a computational linguistics domain. Evaluation results showed that the proposed method could improve the performance of similarity-based baseline approaches.

• Honam Mathematical Journal
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• v.36 no.3
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• pp.519-530
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• 2014

To study a deformation of a digital space from the viewpoint of digital homotopy theory, we have often used the notions of a weak k-deformation retract [20] and a strong k-deformation retract [10, 12, 13]. Thus the papers [10, 12, 13, 16] firstly developed the notion of a strong k-deformation retract which can play an important role in studying a homotopic thinning of a digital space. Besides, the paper [3] deals with a k-deformation retract and its homotopic property related to a digital fundamental group. Thus, as a survey article, comparing among a k-deformation retract in [3], a strong k-deformation retract in [10, 12, 13], a weak deformation k-retract in [20] and a digital k-homotopy equivalence [5, 24], we observe some relationships among them from the viewpoint of digital homotopy theory. Furthermore, the present paper deals with some parts of the preprint [10] which were not published in a journal (see Proposition 3.1). Finally, the present paper corrects Boxer's paper [3] as follows: even though the paper [3] referred to the notion of a digital homotopy equivalence (or a same k-homotopy type) which is a special kind of a k-deformation retract, we need to point out that the notion was already developed in [5] instead of [3] and further corrects the proof of Theorem 4.5 of Boxer's paper [3] (see the proof of Theorem 4.1 in the present paper). While the paper [4] refers some properties of a deck transformation group (or an automorphism group) of digital covering space without any citation, the study was early done by Han in his paper (see the paper [14]).

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.2
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• pp.25-32
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• 2020

Data on patent and scientific paper is considered as a useful information source for analyzing technological information and has been widely utilized. Technology big data is analyzed in various ways to identify the latest technological trends and predict future promising technologies. Clustering is one of the ways to discover new features by creating groups from technology big data. Patent includes refined bibliographic information such as patent classification code whereas scientific paper does not have appropriate bibliographic information for clustering. This research proposes a new approach for clustering data of scientific paper by utilizing reference titles in each scientific paper. In this approach, the reference titles are considered as textual information because each reference consists of the title of the paper that represents the core content of the paper. We collected the scientific paper data, extracted the title of the reference, and conducted clustering by measuring the text-based similarity. The results from the proposed approach are compared with the results using existing methodologies that one is the approach utilizing textual information from titles and abstracts and the other one is a citation-based approach. The suggested approach in this paper shows statistically significant difference compared to the existing approaches and it shows better clustering performance. The proposed approach will be considered as a useful method for clustering scientific papers.

• Journal of Information Science Theory and Practice
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• v.5 no.3
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• pp.48-60
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• 2017

The purpose of this paper is to understand the profiles of users and their motivations in sharing research articles on Twitter. The goal is to contribute to the understanding of Twitter as a new altmetric measure for assessing impact of research articles. In this paper, we extended the previous study of tweet motivations by finding out the profiles of twitter users. In particular, we examined six characteristics of users: gender, geographic distribution, academic, non-academic, individual, and organization. Out of several, we would like to highlight here three key findings. First, a great majority of users (86%) were from North America and Europe indicating the possibility that, if in general, tweets for research articles are mainly in English, Twitter as an alternative metric has a Western bias. Second, several previous altmetrics studies suggested that tweets, and altmetrics in general, do not indicate scholarly impact due to their low correlation with citation counts. This study provides further details in this aspect by revealing that most tweets (77%) were by individual users, 67% of whom were nonacademic. Therefore, tweets mostly reflect impact of research articles on the general public, rather than on academia. Finally, analysis from profiles and motivations showed that the majority of tweets (from 42% to 57%) in all user types highlighted the summary or findings of the article indicating that tweets are a new way of communicating research findings.

• Journal of Technology Innovation
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• v.22 no.4
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• pp.1-19
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• 2014

The purpose of this research is to analyze the performance of technology transfer of university and professors' capability based on their research activities and performance. For this study, paper citation index, patenting activities, R&D funding scales from public and private sectors separately, careers before and after being professor, scale of research team of 80 professors, who had experienced in technology transfer, were collected. Major result of this research shows that patenting activities rather than paper publishing, R&D funding from private sectors rather than public sectors, and careers after being professor rather than before, are significant factors to affect the performance of university's technology transfer.

• The Journal of Korean Academic Society of Nursing Education
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• v.26 no.1
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• pp.5-15
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• 2020

Purpose: This paper is a critical review of descriptive phenomenological methodology in Korean nursing research. We propose constructive suggestions for the improvement of descriptive phenomenological methodology in light of Husserl's phenomenological approaches. Methods: Using the key words of 'phenomenology,' 'experience,' and 'nursing,' we identify and analyze 64 Korean empirical phenomenological studies (selected from 282 studies) published in 14 Korean nursing journals from 2005 to 2018. The PubMed and the Korea Citation Index were used to identify the studies. Results: Our analysis shows that all the reviewed articles used Giorgi's or Colaizzi's scientific phenomenological methodology, without critical attention to Husserl's philosophical phenomenological principles. Conclusions: The use of scientific phenomenology in nursing research, which originated in North America, has become a global phenomenon, and Korean phenomenological nursing research has faithfully followed this scholarly trend. This paper argues that greater integration of Husserlian phenomenological principles into scientific phenomenological methodology in nursing research, such as participant-centered bracketing and eidetic reduction, is needed to ensure that scientific phenomenology lives up to its promise as a research methodology.

• Journal of Information Technology Applications and Management
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• v.24 no.1
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• pp.33-43
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• 2017

The content curation service through big data analysis is receiving great attention in various content fields, such as film, game, music, and book. This service recommends personalized contents to the corresponding user based on user's preferences. The existing book curation systems recommended books to users by using bibliographic citation, user profile or user log data. However, these systems are difficult to recommend books related to character names or spatio-temporal information in text contents. Therefore, in this paper, we suggest a personalized book curation system based on integrated mining of a book. The proposed system consists of mining system, recommendation system, and visualization system. The mining system analyzes book text, user information or profile, and SNS data. The recommendation system recommends personalized books for users based on the analysed data in the mining system. This system can recommend related books using based on book keywords even if there is no user information like new customer. The visualization system visualizes book bibliographic information, mining data such as keyword, characters, character relations, and book recommendation results. In addition, this paper also includes the design and implementation of the proposed mining and recommendation module in the system. The proposed system is expected to broaden users' selection of books and encourage balanced consumption of book contents.

• Journal of Information Science Theory and Practice
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• v.5 no.2
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• pp.48-61
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• 2017

This paper uses 601 cited papers of Pakistani LIS researchers with the purpose to examine the individual performance of these Library and Information Science (LIS) researchers in terms of their research output and its impact on the LIS (national/international) literature by using various bibliometric indicators. A list of 139 authors was compiled with the help of the Library, Information Science, and Technology Abstracts (LISTA) and some other sources. Data were collected from Google Scholar and SPSS version 20 was utilized in order to identify the relationship between self-citations and various performance indices of the authors. The average citations received per paper vary from 1.80 to 10.08. About half of the papers were single-authored whereas less than one-fifth were by three or more authors. The authors who worked in collaboration produced more papers and received more citations. The h-index, g-index, hI-index, hI-norm, and e-index were used to determine the rank for each author. The intra-group citations grid revealed the volume of self-citations and a small group who cite each other more due to close academic and social relationships. The correlations between self-citations and the impact indices used revealed significant differences. Findings are useful for concerned institutions regarding award, promotions, etc. Further, future research should seriously consider the self-citations and social networking of authors while examining their citations-based research performance.