• Journal of Korean Library and Information Science Society
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• v.36 no.2
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• pp.141-163
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• 2005

Today. the value of the references comes to be high and the reference database construction is increasing. But. the descriptive regulations of the reference which it presents from scholarly journals different with each other. Also, the researchers probably does not recognize a descriptive regulations of reference. It is likely that the effective construction of reference database is very difficult. So, the purpose of this study analyzes the differences of a descriptive regulations for reference and presents the plan for standardization of a descriptive regulations.

• Journal of Korean Library and Information Science Society
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• v.43 no.3
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• pp.385-405
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• 2012

This study examined the frequency, format, and activity of Internet references of four major journals in Library and Information Science in Korea. Sampled articles of each journal were published during 2002-2009. The results show five implications. 1) Overall 15.5% of all references were Internet references. 2) The number of Internet references has continuously increased since 2002. 3) The type of Internet references were most commonly "web document(84.3%), followed by" PDF(14%). 4) Nearly 55% of Internet reference were ".or(org)" and ".ac(edu))". 5) The percentage of inactive Internet references increased from 34.4% at 3 years to 44.1% at 5 years and to 53.3% at 10 years after publication. Although this study may not be representative of the entire field, it shows that Internet references occurred frequently and were often inaccessible within years after publication.

• Annual Conference on Human and Language Technology
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• 2018.10a
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• pp.461-464
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• 2018

최근 학술문헌이 급격하게 증가함에 따라, 학술문헌간의 연결성 및 메타데이터 추출 등의 핵심 자원으로서 활용할 수 있는 참고문헌에 대한 활용 연구가 진행되고 있다. 본 연구에서는 국내 학술지의 참고문헌이 가진 각 메타데이터를 자동적으로 인식하여 추출할 수 있는 참고문헌 메타데이터 인식에 대하여, 연속적 레이블링 방법론을 기반으로 접근한다. 심층학습 기술 중 연속적 레이블링에 우수한 성능을 보이고 있는 Bidirectional GRU-GRU CRF 모델을 기반으로 참고문헌 메타데이터 인식에 적용하였으며, 2010년 이후의 10종의 학술지내의 144,786건의 논문을 활용하여 추출한 169,668건의 참고문헌을 가공하여 실험하였다. 실험 결과, 실험집합에 대하여 F1 점수 97.21%의 우수한 성능을 보였다.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10c
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• pp.283-285
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• 2003

논문을 작성하기 위해서는 참고문헌을 달아주어야 하는 쉽지 않은 과정이 있다. 일반 사용자가 쉽게 작성하고 적은 노력으로 참고문헌을 삽입시켜줄 수 있는 처리기가 필요하다. 기존의 LATEX 시스템은 고정된 환경과 많은 제약사항으로 사용하기에 불편이 많았다. 본 논문에서는 XML의 구조적인 특성을 이용하여 참고문헌을 자동으로 삽입하는 새로운 어플리케이션을 제안한다. 이를 위해 논문과 참고문헌 사전에 대한 문서타입을 정의하였으며 참고문헌사전의 구조 정보를 검색, 삽입, 수정, 삭제 할 수 있는 사전 관리기를 설계하였다. 이를 바탕으로 참고문헌을 자동 삽입 처리된 논문 곧 XML문서는 논문 구조에 맞게 정의된 XSL을 통해 HTML로 변환되며 자유로운 웹 게시가 가능해진다.

• Journal of the Korean Society for information Management
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• v.38 no.1
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• pp.221-242
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• 2021

This study applied reference metadata recognition using bidirectional GRU-CRF model based on pre-trained language model. The experimental group consists of 161,315 references extracted by 53,562 academic documents in PDF format collected from 40 journals published in 2018 based on rules. In order to construct an experiment set. This study was conducted to automatically extract the references from academic literature in PDF format. Through this study, the language model with the highest performance was identified, and additional experiments were conducted on the model to compare the recognition performance according to the size of the training set. Finally, the performance of each metadata was confirmed.

• Journal of the Korean Society for Library and Information Science
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• v.36 no.4
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• pp.21-47
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• 2002

The present study, on the bass of a premise that reference lists are one of the social products, reflecting various social environments of their own society, was made as part of an attempt to explore the co-occurrence phenomenon of both Korean and Non-Korean Literatures occurred within the Korean references. 321 authors (articles) of 43 Korean journals on Social Sciences were surveyed on research channels and citation motivations and their 11358 references were analyzed. The findings are as follows : 1) The extent of the co-occurrence was that Non-Korean literatures were more 1.9 times (65.3%) cited than Korean ones and English (61.5%)-American (50.4%) predominancy was heavily found. 2) Research channel, worked as an indicator of the identity of researcher as well as the source of research ideas was most Non-Korean channel orientedness (55.8%). 3) Citation motivations were significantly depended on whether Korean or Non-Korean literatures and non-Korean literatures were cheifly cited to be conceptual motivations than other motivations. 4) Research channel among variables was worked as a main effect predicting major citation motivations on Non-Korean literatures. Finally, this study is very suggestive : 1) It might be a new approach and interpretation by adopting citation motivations to explore a process of knowledge producting of researchers 2) Partly, it proved empirically the relationship of knowledge producted by Korean researchers to Non-Korean knowledge through the analysis of citation motivations.

• Journal of the Korean Society for information Management
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• v.22 no.2 s.56
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• pp.23-39
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• 2005

The purpose of this study was to analyzes the present conditions of reference databases which is constructed from Korea. The object which is used in analysis concludes on KSCI( Korean Science Citation Index) of KISTI, KCI(Korean Citation Index) of KRF, KoMCI(Korean Medical Citation Index) of Korean Academy of Medical Sciences, and reference database of KOSEF. And then, this paper proposes the activation plan for reference database construction based on this analysis result. The proposed plan will be able to apply with fundamental data of the system, policy and technical direction for reference database construction.

• Journal of the Korean Society for information Management
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• v.40 no.2
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• pp.115-135
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• 2023

The purpose of this study is to assess the effectiveness of using deep learning language models to extract references automatically and create a reference database for research reports in an efficient manner. Unlike academic journals, research reports present difficulties in automatically extracting references due to variations in formatting across institutions. In this study, we addressed this issue by introducing the task of separating references from non-reference phrases, in addition to the commonly used metadata extraction task for reference extraction. The study employed datasets that included various types of references, such as those from research reports of a particular institution, academic journals, and a combination of academic journal references and non-reference texts. Two deep learning language models, namely RoBERTa+CRF and ChatGPT, were compared to evaluate their performance in automatic extraction. They were used to extract metadata, categorize data types, and separate original text. The research findings showed that the deep learning language models were highly effective, achieving maximum F1-scores of 95.41% for metadata extraction and 98.91% for categorization of data types and separation of the original text. These results provide valuable insights into the use of deep learning language models and different types of datasets for constructing reference databases for research reports including both reference and non-reference texts.

• Proceedings of the Korean Information Science Society Conference
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• 2000.04b
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• pp.726-728
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• 2000

본 논문에서는 최근 인터넷 상에서 표준 공통 포맷으로 대두되고 있는 XML을 이용하여 웹 기반 원격 교육 시스템에서 강의 내용에 참조도리 참고문헌 지원 시스템을 설계하고 구현하였다. Three-tier 환경에서 구현한 이 시스템은 middle-tier인 웹 서버에서 데이터베이스에 저장된 참고문헌을 XML 데이터로 변환하여 효과적으로 처리함으로써 서버의 부하를 감소시키며 이것은 성능 향상으로 이어져 학습자에게 더 나은 속도로 원격 교육의 참고문헌 정보서비스를 제공할 수 있다. 또한 동적으로 서버와 상호작용 가능하도록 학습자가 c마고 문헌의 유익함 정도를 매긴 등급 점수 계산에 직접 참여시켜 그 결과를 볼 수 있도록 하여 학습 의욕을 더욱 고취시킬 수 있다. 앞으로 웹기반 원격교육의 참고문헌 지원 시스템은 세계 각 대학이나 연구소에 분산되어 있는 여러 데이터 소스로부터 필요한 정보만을 실시간으로 추출하여 수집, 통합, 통계 처리할 수 있도록 확장될 수 있을 것이다.

• Journal of the Korean Society for Library and Information Science
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• v.56 no.3
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• pp.241-264
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• 2022

Bibliographic metadata can help researchers effectively utilize essential publications that they need and grasp academic trends of their own fields. With the manual creation of the metadata costly and time-consuming. it is nontrivial to effectively automatize the metadata construction using rule-based methods due to the immoderate variety of the article forms and styles according to publishers and academic societies. Therefore, this study proposes a two-step extraction process based on rules and deep neural networks for generating bibliographic metadata of scientific articlles to overcome the difficulties above. The extraction target areas in articles were identified by using a deep neural network-based model, and then the details in the areas were analyzed and sub-divided into relevant metadata elements. IThe proposed model also includes a model for generating reference summary information, which is able to separate the end of the text and the starting point of a reference, and to extract individual references by essential rule set, and to identify all the bibliographic items in each reference by a deep neural network. In addition, in order to confirm the possibility of a model that generates the bibliographic information of academic papers without pre- and post-processing, we conducted an in-depth comparative experiment with various settings and configurations. As a result of the experiment, the method proposed in this paper showed higher performance.