The purpose of this research is to develop evaluation indicators for evaluating the educational value of libraries and, for this, frist of all, preliminary evaluation indicators were derived by analyzing comprehensively about 60 domestic and overseas papers which researched on the value of libraries. On the basis of the derived preliminary evaluation indicators, 10 experts were selected and the final evaluation indicators were developed by conducting Delphi survey three times. The final evaluation indicators are composed of five evaluation areas, which are the divided areas of the educational value of libraries, such as the literacy improvement, supporting of learning and education, research supporting and provision of information resources, improvement of educational environment and quality of education, strengthen competence, 13 evaluation items, and 62 evaluation indicators. In the future, it seems that researches to measure the educational value of libraries based on this will have to be conducted.
This paper aims to explore the citation patterns of conference papers in ‘Information Science’ discipline and to analze impacts of various cited works-related factors on future citations of conference papers. Existing bibliometric studies has investigated citation patterns and the statistical relations between a variety of bibliographic factors and the future citations of literature. However, the attentions have been focused largely on journal articles, and the bibliometric studies targeting conference papers are still in an infant stage. Therefore, this study, which is based on 1,904 conference papers in ‘Information Science’ field, examined several citation patterns and the contributions of the factors about cited works – the number of cited works, type of cited works, citation rates and ages of cited works at the time of being cited and the rate of self-citedness – to the future citation of the citing target articles. The data source of this study including the properties of target articles and cited works and citation rates of target articles was Scopus. As the results, 53% and 29% of the cited works were conference papers and journal articles, respectively. 14% of them are non-traditional types such as web pages, technical reports, patent, etc. More than 60% of the cited works were 5 years old or less. Among several factors considered in this paper, the number of conference papers and the number of non-traditional types of works are the most contributing factors on the citation rates of target articles. The recency of the cited works is also significant contributor on the citation rates of target articles. That is, the target articles citing more conference papers and non-traditonal types of works earned more citations. The target articles citing recent works also earned more citations.
In this study, a survey was conducted with 385 students in a junior college, where jobs and field education are emphasized, to identify the factors affecting job information sharing. The structural equation modeling was adopted to analyze the collected data. The most influential factor to the sharing was cultural factor, which was affected by the organizational culture promoting job information sharing, the leadership of top manager, and the appropriate compensation system. The results from this study can be used as important basic data for activating job information sharing among junior college students.
A recent sharp increase of the biomedical literature causes researchers to struggle to grasp the current research trends and conduct creative studies based on the previous results. In order to alleviate their difficulties in keeping up with the latest scholarly trends, numerous attempts have been made to develop specialized analytic services that can provide direct, intuitive and formalized scholarly information by using various text mining technologies such as information extraction and event detection. This paper introduces and evaluates total 8 Convolutional Neural Network (CNN) models for extracting biomedical events from academic abstracts by applying various feature utilization approaches. Also, this paper conducts performance comparison evaluation for the proposed models. As a result of the comparison, we confirmed that the Entity-Type-Fully-Connected model, one of the introduced models in the paper, showed the most promising performance (72.09% in F-score) in the event classification task while it achieved a relatively low but comparable result (21.81%) in the entire event extraction process due to the imbalance problem of the training collections and event identify model's low performance.
In order to develop a semiautomatic support system that allows researchers concerned to efficiently analyze the technical trends for the ever-growing industry and market. This paper introduces a couple of Korean sentence generation models that can automatically generate definitional statements as well as descriptions of technical terms and concepts. The proposed models are based on a deep learning model called LSTM (Long Sort-Term Memory) capable of effectively labeling textual sequences by taking into account the contextual relations of each item in the sequences. Our models take technical terms as inputs and can generate a broad range of heterogeneous textual descriptions that explain the concept of the terms. In the experiments using large-scale training collections, we confirmed that more accurate and reasonable sentences can be generated by CHAR-CNN-LSTM model that is a word-based LSTM exploiting character embeddings based on convolutional neural networks (CNN). The results of this study can be a force for developing an extension model that can generate a set of sentences covering the same subjects, and furthermore, we can implement an artificial intelligence model that automatically creates technical literature.
As information users seek multimedia with a wide variety of information needs, information environments for multimedia have been developed drastically. More specifically, as seeking multimedia with emotional access points has been popular, the needs for indexing in terms of abstract concepts including emotions have grown. This study aims to analyze the index terms extracted from Getty Image Bank. Five basic emotion terms, which are sadness, love, horror, happiness, anger, were used when collected the indexing terms. A total 22,675 index terms were used for this study. The data are three sets; entire emotion, positive emotion, and negative emotion. For these three data sets, co-word occurrence matrices were created and visualized in weighted network with PNNC clusters. The entire emotion network demonstrates three clusters and 20 sub-clusters. On the other hand, positive emotion network and negative emotion network show 10 clusters, respectively. The results point out three elements for next generation of multimedia retrieval: (1) the analysis on index terms for emotions shown in people on image, (2) the relationship between connotative term and denotative term and possibility for inferring connotative terms from denotative terms using the relationship, and (3) the significance of thesaurus on connotative term in order to expand related terms or synonyms for better access points.
The purpose of this study is to find out the influence of reading role model(parents, teachers, and friends) and reading effectiveness(reading satisfaction, willingness to persistent reading, immersed reading) reflected on middle school students’ academic achievement(Korean Language performance and middle school record). As a result of the analysis, the factors affecting the performance of Korean Language were father (1.9%) and mother (3.4%) among reading role models. Among reading effectiveness factors, willingness to persistent reading (8%) was the only effective factor. In the case of the middle school record, the mother (3.4%) had the only effect among the reading role models, and reading effectiveness was influenced all factors such willingness to persistent reading (10.4%), immersed reading (4.0%) and reading satisfaction (1.9%). However, except for the factor willingness to persistent reading, the it found the influence was insignificant.
The present study aimed at investigating health information seeking behaviors of health information consumers who changed their health behaviors by using mobile health applications according to the dynamic stages of change. For this purpose, the study analyzed the changes of perception, health information needs and seeking behaviors of health information consumers in each stage by employing Stages of Change as a theoretical framework. A total of 30 college students participated in this study to change health behaviors such as smoking or alcohol cessation, and regular exercise, while using health applications for 3 months; then written interviews were conducted with these students based on their experiences. Findings indicated that the study participants used diverse information sources, including social media and the Internet, seeking for different types of sources of information according to information needs. Above all, the health information needs and seeking behaviors examined in active utilization of health applications by consumers in the stage of action suggest the implications of health information services, particularly through health applications. In addition, stress management and relapse that consumers experienced while attempting health behavior changes, and the positive and negative effects of behavior changes inform health information providers of insights for supporting consumers’ changes of health behaviors.
The purpose of this study was to examine the current staffing practices in public libraries and to analyze the relevance of legal standards of public library staffing. It analyzed the national library statistical data to obtain the national average staffing rate, reviewed related literature to understand the rationales that established the key attributes that construct the current legal standards, and compared the placement rates of the three standards, namely the current legal standards, Korean Library Association’s standards, and IFLA staffing standards, to determine the optimacy of the legal standards. It found the current placement rate in the legal standard was only 18.2%, marking the lowest rates among the three compared standards, indicating the current legal standards being unnecessarily high. The study attributes the problem to the structure of the current legal standards that calculate the staffing size based on the building and collection size rather than service populations. The findings of this study suggest a revision of the current standard based on service population while retaining the minimum staffing of three librarians even the smallest public library unit.
The advent of the fourth industrial revolution has led to various technologies such as bigdata, the internet of things, artificial intelligence etc. Based on these innovations, the types of information services can changed in the library. The focus is on smart device. This study aims to identify utilization status and service implications of the smart device in the library. To achieve this goal, we conducted current status analysis of the smart device in the library through literature research and online search and gathered the executives views of practical librarians. Consequently, we proposed improvement of library service by using smart device. The results of this study will be expected to help next generation library establish service strategies.
This paper introduces an integrated model for systematically constructing a linguistic resource database that can be used by machine learning-based biomedical information extraction systems. The proposed method suggests an orderly process of collecting and constructing dictionaries and training sets for both named-entity recognition and relation extraction. Multiple heterogeneous structures for the resources which are collected from diverse sources are analyzed to derive essential items and fields for constructing the integrated database. All the collected resources are converted and refined to build an integrated linguistic resource storage. In this paper, we constructed entity dictionaries of gene, protein, disease and drug, which are considered core linguistic elements or core named entities in the biomedical domains and conducted verification tests to measure their acceptability.