• Journal of Information Technology Applications and Management
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• v.17 no.4
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• pp.1-17
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• 2010

As web technologies evolve and so-called Web 2.0 technologies appear, collective intelligence is being applied in widespread areas. In general, mass collective intelligence like Wikipedia is created, revised, and managed by anonymous participants in an uncontrolled system. Thus, the knowledge provided by mass collective intelligence may be distorted, and may not be true, which may affect the user acceptance behavior. However, there have been few academic studies that analyzed the factors that affect user acceptance of mass collective intelligence, and their relationships. Under this academic background, we develop a model to examine how mass collective intelligence is accepted by users. The theoretical model is validated through an online survey of the Wikipedia users from three universities in Korea. The results reveal that the users will have positive attitude towards adopting mass collective knowledge when they perceive that the knowledge from mass collective intelligence is useful. We also find that the perceived usefulness of the knowledge is affected by perceived knowledge quality and trust in knowledge contributors. The results also suggest that perceived knowledge quality is determined by perceived level of collaboration, perceived objectivity, and recipient expertise, whereas trust in knowledge contributors is determined by natural propensity to trust and perceived objectivity. Theoretical and practical implications about mass collective knowledge are discussed.

• The Journal of Korean Academic Society of Nursing Education
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• v.22 no.3
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• pp.264-273
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• 2016

Purpose: The purpose of this study was to investigate humanistic knowledge, emotional intelligence, and communication skills among nurses and to identify factors influencing the communication skills of nurses in five hospitals. Methods: Data were collected from 248 nurses in hospitals from August 25 to September 9, 2015. Data were analyzed with descriptive statistics, t-test, ANOVA, Pearson's correlations and enter method regression analysis using the SPSS/Win 22.0 program. Results: There were significant differences in humanistic knowledge according to religion, education, work department, number of night duty, pay level satisfaction, job satisfaction, and job performance. There were significant differences in emotional intelligence according to all general characteristics. There were significant differences in communication skills according to education, position, number of night duty, and job performance. Communication skills showed positive correlations with humanistic knowledge and emotional intelligence. Emotional intelligence correlated positively with humanistic knowledge. Emotional intelligence was a significant predictor and accounted for 32% of variance in the communication skills. Conclusion: To strengthen communication skills, programs need to be complemented in order to promote humanistic knowledge and emotional intelligence for nurses.

• Knowledge Management Research
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• v.19 no.3
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• pp.173-187
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• 2018

This study was aimed at suggesting an integrative framework for creating collective intelligence and enhancing group performance after reviewing previous studies including those related to learning organizations, organizational learning, knowledge management, and collective intelligence. In the first, we examined that the similarities and differences between collective intelligence and other similar concepts, such as learning organizations, organizational learning, and knowledge management. Next, an integrative framework for creating collective intelligence and channeling it into strong group performance were suggested. In this process, we reviewed conditions for creating collective intelligence and segmented the major variables as expectancy, valence, and instrumentality, according to Vroom's (1964) expectancy theory. Characteristics of problems and the roles of leaders were respectively considered as valence for inducing collaboration and expectancy for managing probability to achieve goals. Instrumental factors were also adopted from conditions for creating group intelligence suggested from several researchers, such as creativity, openness, willingness for working together, horizontal communication, centralization in decision making, and building effective information and communication technology system and active usage of it. We discussed two potentially disputable matters about the scope and level of collective intelligence and group performance and suggest several theoretical and practical implications in the Discussion.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1050-1053
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• 2005

There is no agreed definition of intelligence. The ability to adapt to the environments is a kind of intelligence. Expert functionally recognize environment using their five senses, and acquire and memorize knowledge necessary for operating machines. Knowledge that they cannot acquire directly is acquired in indirect ways. The purpose of intelligence machines is applying to machines experts' knowledge acquisition process and their skills in operating machine. An agent is an autonomous process that recognizes external environment, exchanges knowledge with external machines and performs an autonomous decision-making function in order to achieve common goals. This paper describes agent application for intelligence machine.

• Industrial Engineering and Management Systems
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• v.14 no.1
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• pp.73-87
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• 2015

The development of a practical technology intelligence system requires a knowledge base that structures the core information and its relationship distilled from large volumes of technical data. Previous studies have mainly focused on the methodological approaches for technology opportunities, while little attention has been paid to constructing a practical knowledge base. Therefore, this study proposes a procedure to construct a function-based knowledge base for technology intelligence. We define the product-function-technology relationship and subsequently present the detailed steps for the knowledge base construction. The knowledge base, which is constructed analyzing 1110582 patents between 2009 and 2013 from the United States Patent and Trademark Office database, contains the functional knowledge of products and technologies and the relationship between products and technologies. This study is the first attempt to develop a large-scale knowledge base using the concept of function and has the ability to serve as a basis not only for furthering technology opportunity analysis methods but also for developing practical technology intelligence systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.2
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• pp.691-708
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• 2014

This paper formalizes a special type of social networking knowledge, which is called "workflow performer-role affiliation networking knowledge." A workflow model specifies execution sequences of the associated activities and their affiliated relationships with roles, performers, invoked-applications, and relevant data. In Particular, these affiliated relationships exhibit a stream of organizational work-sharing knowledge and utilize business process intelligence to explore resources allotting and planning knowledge concealed in the corresponding workflow model. In this paper, we particularly focus on the performer-role affiliation relationships and their implications as organizational and business process intelligence in workflow-driven organizations. We elaborate a series of theoretical formalisms and practical implementation for modeling, discovering, and visualizing workflow performer-role affiliation networking knowledge, and practical details as workflow performer-role affiliation knowledge representation, discovery, and visualization techniques. These theoretical concepts and practical algorithms are based upon information control net methodology for formally describing workflow models, and the affiliated knowledge eventually represents the various degrees of involvements and participations between a group of performers and a group of roles in a corresponding workflow model. Finally, we summarily describe the implications of the proposed affiliation networking knowledge as business process intelligence, and how worthwhile it is in discovering and visualizing the knowledge in workflow-driven organizations and enterprises that produce massively parallel interactions and large-scaled operational data collections through deploying and enacting massively parallel and large-scale workflow models.

• 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.

• Knowledge Management Research
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• v.14 no.5
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• pp.137-149
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• 2013

Although business Intelligence system is introduced to many companies over the past decade, The result of business benefits from BI investment are not so significant than expected. But still successful BI system can provide the ability to analyse business information in order to support and improve management decision making across a broad range of business activities. In recently, Business Analytics System(BA) is emerging as advanced alternative of outdated and inefficient BI System. This study is focus on constructing procedure of BA system in KB card company, which is major credit card company in South Korea. In practice there were just few works that mentioned well-designed environment of KMS system, and other contribution of this study is to make a platform which invoke revelation of collective intelligence in data analytic professional users group.

• Korean Institute of Interior Design Journal
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• no.7
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• pp.70-78
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• 1996

Artificial Intelligence is generally concerned with tasks whose execution appears to involve some intelligence if done by humans, and knowledge-based system ( in other word, expert system) is the research about the specific domain. This concept also can be applied to interior design field. So the purpose of this study is in reconstructing the accomplishment of artificial Intelligence and knowledge engineering, searching basic theories and cased to knowledge engineering , searching basic theories and cases to formulate knowledge -based design system, and testing the posibilities how the design information can be dealt in computer system. Given that recognition , two major problems must be solved before knowledge-based CAD systems could be come practical : Firstly , identification of the interior of designers use .Secondly , representing this knowledge in a computationally effective manner. I had discussed the basic concepts on which to base a knowledge- based design model, knowledge representation schemes, and problem solving, I could find the possibility which the knowledge-based system can be applied to the interior design according to this study. But there are non-deductive, often irrational and now easily computerized design process in interior design. Those are problems which are relevant to the machine learning and the creativity in design. So there should be a lot of research about the machine learning and the creatively in design in order to construct successfully intelligent knowledge-based design system.

• The Journal of the Korea Contents Association
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• v.22 no.1
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• pp.444-455
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• 2022

The purpose of this study is to examine the roles of emotional intelligence and knowledge sharing, while focusing on the customer orientation of flight attendants, to identify the strategies to improve the quality of services provided by flight attendants. For this purpose, the researcher examined the relationships between emotional intelligence and customer orientation, as well as the impact of knowledge sharing in the process. The survey was conducted over three weeks from May 6, 2021, to May 27, 2021, and the 426 questionnaires recovered were analyzed using statistical software programs SPSS 23.0 and AMOS 23.0. The findings of this study are as follows; First, the emotional intelligence of flight attendants had a positive impact on customer orientation. Second, the emotional intelligence of flight attendants had a positive impact on knowledge sharing. Third, the knowledge sharing of flight attendants had a positive impact on customer orientation. Fourth, in the relationship between the emotional intelligence of flight attendants and their customer orientation, knowledge sharing had a significant mediating effect. Based on the findings of this study, it would be possible to consider support strategies for organizations, including the training programs for enhancing the emotional intelligence of flight attendants and a corporate culture in which knowledge sharing can be promoted.