• Industrial Engineering and Management Systems
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• v.15 no.3
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• pp.215-223
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• 2016

Design of personnel teams has been studied from diverse perspectives; the most common are the people and systems requirements perspectives. All these point of view are linked, which is the reason why it is necessary to study them simultaneously. Considering this gap, a decision making model is developed based on factors, models, and requirements mentioned in the literature. The model is applied to a real case. The findings indicate that the Personnel Behavior Based Lean model (PBBL) can be converted into a decision making model for the selection of team members. The study is focused not only on the individual candidates' knowledge, skills, and aptitudes, but also on how the model considers the company requirements, conflicts, and the importance of each person to the project.

• International Journal of Knowledge Content Development & Technology
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• v.7 no.1
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• pp.101-120
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• 2017

Healthcare professionals have edited many health questions (HQs) and their answers for healthcare consumers on the Internet. The HQs provide both readable and reliable health information, and hence retrieval of those HQs that are relevant to a given question is essential for health education and promotion through the Internet. However, retrieval of relevant HQs needs to be based on the recognition of the intention of each HQ, which is difficult to be done by predefining syntactic and semantic rules. We thus model the intention recognition problem as a text classification problem, and develop two techniques to improve a learning-based text classifier for the problem. The two techniques improve the classifier by location-based and area-based feature weightings, respectively. Experimental results show that, the two techniques can work together to significantly improve a Support Vector Machine classifier in both the recognition of HQ intentions and the retrieval of relevant HQs.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.43-61
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• 2019

Development of technologies in artificial intelligence has been rapidly increasing with the Fourth Industrial Revolution, and researches related to AI have been actively conducted in a variety of fields such as autonomous vehicles, natural language processing, and robotics. These researches have been focused on solving cognitive problems such as learning and problem solving related to human intelligence from the 1950s. The field of artificial intelligence has achieved more technological advance than ever, due to recent interest in technology and research on various algorithms. The knowledge-based system is a sub-domain of artificial intelligence, and it aims to enable artificial intelligence agents to make decisions by using machine-readable and processible knowledge constructed from complex and informal human knowledge and rules in various fields. A knowledge base is used to optimize information collection, organization, and retrieval, and recently it is used with statistical artificial intelligence such as machine learning. Recently, the purpose of the knowledge base is to express, publish, and share knowledge on the web by describing and connecting web resources such as pages and data. These knowledge bases are used for intelligent processing in various fields of artificial intelligence such as question answering system of the smart speaker. However, building a useful knowledge base is a time-consuming task and still requires a lot of effort of the experts. In recent years, many kinds of research and technologies of knowledge based artificial intelligence use DBpedia that is one of the biggest knowledge base aiming to extract structured content from the various information of Wikipedia. DBpedia contains various information extracted from Wikipedia such as a title, categories, and links, but the most useful knowledge is from infobox of Wikipedia that presents a summary of some unifying aspect created by users. These knowledge are created by the mapping rule between infobox structures and DBpedia ontology schema defined in DBpedia Extraction Framework. In this way, DBpedia can expect high reliability in terms of accuracy of knowledge by using the method of generating knowledge from semi-structured infobox data created by users. However, since only about 50% of all wiki pages contain infobox in Korean Wikipedia, DBpedia has limitations in term of knowledge scalability. This paper proposes a method to extract knowledge from text documents according to the ontology schema using machine learning. In order to demonstrate the appropriateness of this method, we explain a knowledge extraction model according to the DBpedia ontology schema by learning Wikipedia infoboxes. Our knowledge extraction model consists of three steps, document classification as ontology classes, proper sentence classification to extract triples, and value selection and transformation into RDF triple structure. The structure of Wikipedia infobox are defined as infobox templates that provide standardized information across related articles, and DBpedia ontology schema can be mapped these infobox templates. Based on these mapping relations, we classify the input document according to infobox categories which means ontology classes. After determining the classification of the input document, we classify the appropriate sentence according to attributes belonging to the classification. Finally, we extract knowledge from sentences that are classified as appropriate, and we convert knowledge into a form of triples. In order to train models, we generated training data set from Wikipedia dump using a method to add BIO tags to sentences, so we trained about 200 classes and about 2,500 relations for extracting knowledge. Furthermore, we evaluated comparative experiments of CRF and Bi-LSTM-CRF for the knowledge extraction process. Through this proposed process, it is possible to utilize structured knowledge by extracting knowledge according to the ontology schema from text documents. In addition, this methodology can significantly reduce the effort of the experts to construct instances according to the ontology schema.

• STI Policy Review
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• v.4 no.2
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• pp.19-40
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• 2013

The Association of Southeast Asian Nations (ASEAN) achieved relatively rapid economic growth over the past decade. Sustainable growth among member states, however, is put into question due to macroeconomic challenges, political risk, and vulnerability to external shocks. Developed countries, in contrast, have turned into less labor-intensive technologies to further expand their economies. In this paper, we review the science, technology, and innovation (STI) policies and statuses of the scientific and technological capabilities of the ASEAN member countries. Empirical results based on STI indicators (R&D spending, publications, patents, and knowledge economy indices) reveal considerable variation between the science and technology (S&T) competence and effectiveness of STI policies of ASEAN members. We have categorized nations into clusters according their situations in their S&T productivity. Under the Korean Innovation Model, Cambodia, Laos, Myanmar, and Brunei are classified as being in the institutional-building stage, while Malaysia, Thailand, Indonesia, the Philippines, and Vietnam in the catch up stage, and Singapore in the post-catch up stage. Finally, policy prescriptions on how to enhance the S&T capabilities of the developing ASEAN countries, based on the South Korea development experience, are presented.

• Journal of the Society of Naval Architects of Korea
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• v.51 no.2
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• pp.138-147
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• 2014

This paper proposed an application of expert system to submarine arrangement design. Since all components of the submarine should be placed in a restricted place called pressure hull, expert knowledge has great effects on design of submarine arrangement. In this regard, a suitable knowledge-based expert system shell was applied to design of submarine arrangement process. To use expert system on the design of submarine arrangement effectively, a template model for submarine arrangement, which is proper to use in optimum design process, was developed. The proposed system was applied to simplified example of submarine arrangement problem to choose optimal design alternative. From this study, it was verified that expert system could be used in design of submarine arrangement with effect.

• Journal of Information Processing Systems
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• v.10 no.2
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• pp.271-282
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• 2014

The advancement of knowledge society has enabled the social network community (SNC) to be perceived as another space for learning where individuals produce, share, and apply content in self-directed ways. The content generated within social networks provides information of value for the participants in real time. Thus, this study proposes the social network community activity-based content model (SoACo Model), which takes SNC-based activities and embodies them within learning objects. The SoACo Model consists of content objects, aggregation levels, and information models. Content objects are composed of relationship-building elements, including real-time, changeable activities such as making friends, and participation-activity elements such as "Liking" specific content. Aggregation levels apply one of three granularity levels considering the reusability of elements: activity assets, real-time, changeable learning objects, and content. The SoACo Model is meaningful because it transforms SNC-based activities into learning objects for learning and teaching activities and applies to learning management systems since they organize activities -- such as tweets from Twitter -- depending on the teacher's intention.

• Korean Management Science Review
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• v.29 no.1
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• pp.73-88
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• 2012

Much of knowledge that can retain a company's competitive advantage remains within the organization. However, identifying, finding and leveraging knowledge in an organization is still problematic [17]. Although knowledge is the key to success, it is simply too valuable to be left in an organization [59]. The CoP (Community of Practice) within an organization is a practical way to manage knowledge in an organization as systematically as other critical assets in order to deploy and share it [59]. However, research related with CoP, has focused on the value of CoP. Therefore, we developed not only a holistic and systematic method for understanding and assessing the current position of CoP but also a method for extracting the core strategies and CSFs of CoP based on performance evaluation. We developed strategies, CSF (Critical Success Factor) and PM (Performance Measure)s of CoP based on BSC (Balanced Scorecard) process. Specifically, CSFs and strategies of CoP were extracted based on maturity model and type of CoP. According to the procedure from the methodology to evaluate the performance of CoP, three different industrial cases were adopted to validate the evaluation methodology.

• The Journal of Korean Academic Society of Nursing Education
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• v.30 no.1
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• pp.5-18
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• 2024

Purpose: This study aimed to identify whether there is a difference between an online-based community psychiatric nursing practice program with the ARCS model and a conventional community psychiatric nursing practice program in promoting nursing students' learning motivation, knowledge of community psychiatric nursing, communication skills, and learning self-efficacy. Methods: This study used a quasi-experimental design with a non-equivalent control group. The participants were 44 nursing students at three nursing colleges in Gyeongsangbuk-do. The experimental group was provided the online-based community psychiatric nursing practice program with ARCS model, while the control group was provided the conventional community psychiatric nursing practice program from July 9, to September 3, 2022. Both groups received practice training 8 hours a day, 5 days two weeks. The collected data were analyzed using the exact χ², Mann-Whitney U-test, and Quade's two-way ANCOVA with the IBM SPSS Statistics 28.0 program. Results: The results of the comparison of an experimental group training with the online-based community psychiatric nursing practice program with ARCS model and a control group training with the conventional community psychiatric nursing practice program showed that, there was no statistically significant difference between the two groups in learning motivation knowledge of community psychiatric nursing, and learning self-efficacy. However, communication skills were statistically significantly higher in the experimental group (F=6.23, p=.017). Conclusion: The online-based community psychiatric nursing practice program with ARCS model can be used as a substitute learning to improve community psychiatric nursing capabilities in situations when clinical practice is suspended due to infectious diseases such as coronavirus disease 2019 or when is a shortage of community psychiatric nursing practice institutions.

• Journal of Navigation and Port Research
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• v.43 no.3
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• pp.160-165
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• 2019

The conduct of seafarer is major cause of marine accidents. This study models the behavior of the seafarer based on the Hidden Markov Model (HMM). Additionally, through the path analysis of the behavior estimated by the model, the kind of situations, procedures and errors that may have caused the marine accidents were interpreted. To successfully implement the model, the seafarer behaviors were observed by means of the summarized verdict reports issued by the Korean Maritime Safety Tribunal, and the observed results converted into behavior data suitable for HMM learning through the behavior classification framework based on the SRKBB (Skill-, Rule-, and Knowledge-Based Behavior). As a result of modeling the seafarer behaviors by the type of vessels, it was established that there was a difference between the models, and the possibility of identifying the preferred path of the seafarer behaviors. Through these results, it is expected that the model implementation technique proposed in this study can be applied to the prediction of the behavior of the seafarer as well as contribute to the prioritization of the behavior correction among seafarers, which is necessary for the prevention of marine accidents.

• Journal of Information Technology Services
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• v.12 no.3
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• pp.253-271
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• 2013

Innovation becomes norm rather than exception in today's business, and accordingly firms are working on how to make their employees to work smarter using information systems and technologies. Smart work demands virtual collaboration and cooperation among team members in different places and different time. Sharing of knowledge among team members in these innovative activities are critical in every sense for the successful performance. This study explores the antecedents of knowledge sharing among team members in team-based innovation activities. Five factors (pleasure of knowledge sharing, self-efficacy, management support, rewards, and system usage) are identified through extant review of literature and an instrument is adopted and validated from previous studies. The instrument is adminitered against 138 individuals in and across 54 teams in a telecommunication firm. Except self efficacy, all the paths in the proposed research model is confirmed with different levels of relational coefficients towards the levels of knowledge sharing and innovation activities in teams. Surprisingly, findings indicate that intrinsic pleasure of sharing is most critical than management support, organizational rewards or system usage. This study fills the research gap in team management. Findings provide important implications for managing teams in coming virtual and smart environment.