• International journal of advanced smart convergence
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• v.12 no.3
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• pp.75-88
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• 2023

In a specific domain, experts have different understanding of domain knowledge or different purpose of constructing ontology. These will lead to multiple different ontologies in the domain. This phenomenon is called the ontology heterogeneity. For research fields that require cross-ontology operations such as knowledge fusion and knowledge reasoning, the ontology heterogeneity has caused certain difficulties for research. In this paper, we propose a novel ontology matching model that combines word embedding and a concatenated continuous bag-of-words model. Our goal is to improve word vectors and distinguish the semantic similarity and descriptive associations. Moreover, we make the most of textual and structural information from the ontology and external resources. We represent the ontology as a graph and use the SimRank algorithm to calculate the structural similarity. Our approach employs a similarity queue to achieve one-to-many matching results which provide a wider range of insights for subsequent mining and analysis. This enhances and refines the methodology used in ontology matching.

• Journal of Electrical Engineering and information Science
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• v.1 no.1
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• pp.1-9
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• 1996

A key issue in spoken language processing has become the integration of speech understanding and natural language processing(NLP). This paper presents a parallel computational model for the integration of speech and NLP. The model adopts a hierarchically-structured knowledge base and memory-based parsing techniques. Processing is carried out by passing multiple markers in parallel through the knowledge base. Speech-specific problems such as insertion, deletion, and substitution have been analyzed and their parallel solutions are provided. The complete system has been implemented on the Semantic Network Array Processor(SNAP) and is operational. Results show an 80% sentence recognition rate for the Air Traffic Control domain. Moreover, a 15-fold speed-up can be obtained over an identical sequential implementation with an increasing speed advantage as the size of the knowledge base grows.

• Journal of the Economic Geographical Society of Korea
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• v.13 no.2
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• pp.219-233
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• 2010

In this study, the possibility of the application of knowledge management concept in a unit of regional industrial cluster is explored based on diverse case studies. For this purpose, a new framework for knowledge management strategies in an industrial cluster was developed and a model of Knowledge Hub was suggested for the support of integrated knowledge management in an industrial cluster. Additionally, characteristics of Knowledge Hub that should be considered in the design of the Hub are discussed. The concept of Knowledge Hub in this study could be particularly useful for the promotion of innovations in linking clusters and provincial industrial clusters.

• Asia pacific journal of information systems
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• v.23 no.1
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• pp.107-132
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• 2013

Despite the importance of knowledge complementarities (KC) to firm performance, very little is known about exactly what constitutes KC and how synergistic knowledge is created in KC. This research looks into the dimensionality of KC and how synergistic knowledge as an essential component of KC is generated in a process innovation (PI) project. We propose that KC consists of potential complementary knowledge, collaborative elaboration (CE) process, and synergistic knowledge. The model is investigated quantitatively, using a sample of 26 matched-pairs of client and consultant who participated in a PI project, and then qualitatively using interviews of a sub-sample of 7 matched-pairs of client and consultant. Data were collected in a longitudinal way at four different points during the four month project period. Results show that consultant's learning about the client's business occurs first and then client learning about IT capabilities follows through CE. With this enhanced clients' knowledge about IT capabilities, clients play an initiative role in designing the To-Be business processes, while consultants play a supporting role by introducing best practices or making suggestions based on their experiences. Future research implications as well as practical implications are also discussed.

• Journal of Korean Academy of Nursing
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• v.45 no.6
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• pp.802-811
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• 2015

Purpose: The purpose of this study was to test and validate a model to predict living and brain death organ donation intention in nursing students. The conceptual model was based on the theory planned behavior. Methods: Quota sampling methodology was used to recruit 921 nursing students from all over the country and data collection was done from October 1 to December 20, 2013. Results: The model fit indices for the hypothetical model were suitable for the recommended level. Knowledge, attitude, subjective norm and perceived behavioral control explained 40.2% and 40.1% respectively for both living and brain death organ donation intention. Subjective norm was the most direct influential factor for organ donation intention. Knowledge had significant direct effect on attitude and indirect effect on subjective norm and perceived behavioral control. These effects were higher in brain death organ donation intention than in living donation intention. Conclusion: The overall findings of this study suggest the need to develop systematic education programs to increases knowledge about brain death organ donation. The development, application, and evaluation of intervention programs are required to improve subjective norm.

• Journal of the Korean Society of Food Culture
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• v.27 no.1
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• pp.19-29
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• 2012

The purpose of this study was to understand interrelationships among switching costs, customer satisfaction, and switching intent in a family restaurant. Based on a total of 427 customers obtained from empirical research, this study reviewed the reliability and fitness of the research model and verified a total of five hypotheses using the Amos program. The hypothesized relationships in the model were tested simultaneously by using a structural equation model (SEM). The proposed model provided an adequate fit to the data, ${\chi}^2$=137.881 (df=50); p< .001; CMIN/df 2.758; GFI= .947; AGFI= .919, NFI= .965; IFI= .978; TLI= .970; CFI= .978; RMR= .047; RMSEA= .064. The results showed that switching cost (${\beta}$= .123) in a family restaurant had a positive (+) influence upon customer satisfaction. Further, switching cost had a significantly negative (-) effect on switching intent (${\beta}$= -.414). In addition, there were moderating effects related to customer knowledge and variety seeking orientation in terms of the causal relationships between switching costs, customer satisfaction, and switching intent. Limitations and future research directions are also discussed.

• Journal of Engineering Education Research
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• v.22 no.1
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• pp.39-47
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• 2019

This study aims to suggest a model and instructional strategies for a flipped classroom using First Principles of Instruction in engineering education in order to organize teaching and learning activities in a flipped classroom. For this purpose, the authors analyzed the literature on the flipped classroom in engineering education and on applying First Principles of Instruction in designing flipped classroom. Then, a framework of flipped classroom employing First Principles of Instruction and instructional strategies were suggested. Two experts examined the validity of the model and of the instructional strategies, and the final version was completed reflecting on those feedback. Since engineering education aims to teach procedural knowledge as well as conceptual knowledge, different instructional strategies upon two types of knowledge were presented. The implication of our work is to illustrate the model and tactics for flipped classroom based on the Merrill's deeply rooted pedagogical approach. This study may contribute to practice in engineering education.

• The Journal of Information Systems
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• v.29 no.4
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• pp.57-76
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• 2020

Purpose Although examining the antecedents of employees' extra-role behavior (i.e. information security participation behavior) in the information security context is significant for researchers and practitioners, most behavioral security studies have focused on employees' in-role behavior (i.e. information security policy compliance). Thus, this research addresses this gap by investigating how organizational information security climate influences information security participation behavior based on social information processing theory and Griffin and Neal's safety model. Design/methodology/approach We developed a research model by applying Griffin and Neal's safety model to the information security context and then tested our research model by conducting an online survey for employees of organizations with information security policies. Structural equation modeling (SEM) with SmartPLS 3.3.2 is used to test the corresponding hypothesis. Findings Our results show that organizational information security climate, information security knowledge, information security motivation are effective in motivating information security participation behavior. Also, we find that organizational information security climate positively influences both information security knowledge and information security motivation. Our findings emphasize the importance of organizational information security climate because it is capable of affecting employees on information security participation behavior. Our study contributes to the literature on information security by exploring the role of organizational information security climate in enhancing employees' information security participation behavior.

• International Journal of Knowledge Content Development & Technology
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• v.12 no.2
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• pp.57-75
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• 2022

Currently, it is not easy for most domestic public libraries to collect and provide reliable health information services on their own. Health information is distributed or professional, making it inconvenient for users to use. Based on the implications derived from the case study, the Library Health Information Integration Service Model was proposed as a specialized information service. The model consists of a composition shared by librarians, health and medical experts, and users, focusing on library websites that provide integrated health information integration services, and has the following features. First, it provides health and medical information on a specialized subject. Second, it provides integrated health and medical information services provided in various ways. Third, librarians and health and medical experts work together to provide information services. Fourth, users can freely use health information integration services online and offline. The model presented in this study means that libraries can play a leading role in health information integration services to increase the utilization rate of public libraries and further contribute to librarians serving as experts in health information services.

• The Journal of Society for e-Business Studies
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• v.26 no.3
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• pp.67-80
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• 2021

The node embedding technique for learning graph representation plays an important role in obtaining good quality results in graph mining. Until now, representative node embedding techniques have been studied for homogeneous graphs, and thus it is difficult to learn knowledge graphs with unique meanings for each edge. To resolve this problem, the conventional Triple2Vec technique builds an embedding model by learning a triple graph having a node pair and an edge of the knowledge graph as one node. However, the Triple2 Vec embedding model has limitations in improving performance because it calculates the relationship between triple nodes as a simple measure. Therefore, this paper proposes a feature extraction technique based on a graph convolutional neural network to improve the Triple2Vec embedding model. The proposed method extracts the neighborliness vector of the triple graph and learns the relationship between neighboring nodes for each node in the triple graph. We proves that the embedding model applying the proposed method is superior to the existing Triple2Vec model through category classification experiments using DBLP, DBpedia, and IMDB datasets.