• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.4
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• pp.133-141
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• 2012

Virtual project organization may be recognized as one of the promising business models in which many knowledge sources externalize through cross boundaries of knowledge-based organizations. This paper proposes a knowledge competence evaluation of virtual project organization based on the following perspectives: 1) Individual knowledge perspective, 2) Activity-oriented knowledge perspective, and 3) Knowledge-driven social network perspective. In the framework, individual knowledge competence having experienced or learned from knowledge-based activities and virtual networks in the project, should be evaluated according to the assumption that knowledge and collaboration competence depends on the activities and networks acquired proportionally by the past participation to projects. An illustrative SI example is given in order to validate the proposed evaluation and computing procedure.

• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.3
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• pp.1-27
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• 2009

Since online knowledge networks usually consist of a larger, loosely knit, and geographically distributed group of "strangers" who may not know each other very well, members may not willingly share their knowledge with others. In order to address this challenge, this study looks Into the factors that are expected to affect knowledge sharing in an online knowledge network. For empirical validation, we choose "the global network of Korean scientists and engineers (KOSEN)" as one of the best practices of online knowledge networks. By using the archival, network, and survey data, we validate two models of knowledge sharing in sequence (i.e., knowledge contribution and knowledge utilization models) and then discuss the results. The findings of this study show that individuals not only contribute but also utilize knowledge in an online knowledge network when they are structurally embedded and perceive a strong reciprocity. In the network. In addition, taking pleasure in helping is found to positively affect knowledge contribution, whereas perceiving usefulness is found to Influence knowledge utilization. Contributions of this study and future research opportunities are also discussed.

• Knowledge Management Research
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• v.12 no.4
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• pp.17-40
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• 2011

This study examines the differences of enacting models and influential causes of benefit-sharing practices between Korean automobile networks and the Japanese networks. The case study method is chosen for this research because only small numbers of supply networks adopt benefit-sharing practices. I employ semi-structured interviews with managers from four automobile manufacturers and eight of their suppliers in South Korea and Japan. I find that Japanese automobile networks have adopted a higher level of trust-demanding, with a higher level of value-creating models such as supplier development, joint-new-product development. Whereas, the Korean networks have adopted the lower trust demanding, also less profitable models such as supplier's suggestion and buyer's suggestion. In terms of work-related cultural values, I find that Japanese networks emphasized collectivism. Both buyers and suppliers in the Japanese networks are supposed to have common causes. In contrast, Korean networks emphasized individualism. Both buyers and suppliers of Korea generally do not identify that they are common group members with a common cause. I also find that a slight differences of the enacting models and the causes between foreign-owned networks and domestic-owned networks within each country. Foreign-owned networks have adopted lower trust demanding, also less profitable models. The findings demonstrate that the cultural values have a decisive influence on the adoption of benefit sharing models for the networks in Japan, and South Korea.

• Journal of the Korean Geographical Society
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• v.37 no.2
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• pp.111-130
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• 2002

Major purposes of this stud? are to analyze Korean firms'innovation networks and sources of knowledge for innovation and to understand their spatial dimensions. In the innovation networks, parent firms are most important for subcontracting firms, while suppliers, customers and competitors are relatively important for independent firms. However, in the future innovation networks, it is expected that government-sponsored research institutions and university wilt become more important on the one hand, networks with foreign firms will become more important on the other hand. Regarding the process of innovation, distance does not matter for the acquisition of codified knowledge. Spatial proximity is, however, critical for the acquisition of tacit knowledge because discussions and researches in a research division within a firm, personal networks of CEO and workers who are responsible for innovation activity, and inter-firm relations with suppliers and customer in a region are regarded important as sources of tacit knowledge. Overall, the innovation networks are different between the Capital Region and non-Capital Region as well as between the industrial complex and non-industrial complex, suggesting that different regional innovation strategies and policies should be established and implemented by considering such regional specificities. Finally, based on the results of this study several policy implications are suggested.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1442-1453
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• 2016

Now, there are prodigiously expanding worldwide economic networks in the information society, which require their social structural changes through technology innovations. This paper so tries to formally define a process-centered knowledge model to be used to analyze policy-making procedures on technology innovations. The eventual goal of the proposed knowledge model is to apply itself to analyze a topic network based upon composite keywords from a document written in a natural language format during the technology innovation procedures. Knowledge model is created to topic network that compositing driven keyword through text mining from natural language in document. And we show that the way of analyzing knowledge model and automatically generating feature keyword and relation properties into topic networks.

• ETRI Journal
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• v.38 no.6
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• pp.1229-1239
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• 2016

For efficient interaction between humans and robots, robots should be able to understand the meaning and intention of human behaviors as well as recognize them. This paper proposes an interactive human intention reading method in which a robot develops its own knowledge about the human intention for an object. A robot needs to understand different human behavior structures for different objects. To this end, this paper proposes a hierarchical behavior knowledge network that consists of behavior nodes and directional edges between them. In addition, a human intention reading algorithm that incorporates reinforcement learning is proposed to interactively learn the hierarchical behavior knowledge networks based on context information and human feedback through human behaviors. The effectiveness of the proposed method is demonstrated through play-based experiments between a human and a virtual teddy bear robot with two virtual objects. Experiments with multiple participants are also conducted.

• Journal of Intelligence and Information Systems
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• v.9 no.2
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• pp.19-38
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• 2003

In this research, we propose the mechanism to develop self-evolving expert systems (SEES) based on data mining (DM), fuzzy neural networks (FNN), and relational database (RDB)-driven forward/backward inference engine. Most researchers had tried to develop a text-oriented knowledge base (KB) and inference engine (IE). However, this approach had some limitations such as 1) automatic rule extraction, 2) manipulation of ambiguousness in knowledge, 3) expandability of knowledge base, and 4) speed of inference. To overcome these limitations, knowledge engineers had tried to develop an automatic knowledge extraction mechanism. As a result, the adaptability of the expert systems was improved. Nonetheless, they didn't suggest a hybrid and generalized solution to develop self-evolving expert systems. To this purpose, we propose an automatic knowledge acquisition and composite inference mechanism based on DM, FNN, and RDB-driven inference engine. Our proposed mechanism has five advantages. First, it can extract and reduce the specific domain knowledge from incomplete database by using data mining technology. Second, our proposed mechanism can manipulate the ambiguousness in knowledge by using fuzzy membership functions. Third, it can construct the relational knowledge base and expand the knowledge base unlimitedly with RDBMS (relational database management systems) module. Fourth, our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy relationships. Fifth, RDB-driven forward and backward inference time is shorter than the traditional text-oriented inference time.

• International Journal of Knowledge Content Development & Technology
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• v.3 no.1
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• pp.81-95
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• 2013

Through a series of precise text handling processes, including automatic extraction of information from documents with knowledge from various fields, recognition of entity names, detection of core topics, analysis of the relations between the extracted information and topics, and automatic inference of new knowledge, the most efficient knowledge base of the relevant field is created, and plans to apply these to the information knowledge management and service are the core requirements necessary for intellectualization of information. In this paper, the knowledge base, which is a necessary core resource and comprehensive technology for intellectualization of science and technology information, is described and the usability of academic information services using it is evaluated. The knowledge base proposed in this article is an amalgamation of information expression and knowledge storage, composed of identifying code systems from terms to documents, by integrating terminologies, word intelligent networks, topic networks, classification systems, and authority data.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.99-104
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• 2003

In this research, we proposed the mechanism to develop self evolving expert systems (SEES) based on data mining (DM), fuzzy neural networks (FNN), and relational database (RDB)-driven forward/backward inference engine. Most former researchers tried to develop a text-oriented knowledge base (KB) and inference engine (IE). However, thy have some limitations such as 1) automatic rule extraction, 2) manipulation of ambiguousness in knowledge, 3) expandability of knowledge base, and 4) speed of inference. To overcome these limitations, many of researchers had tried to develop an automatic knowledge extraction and refining mechanisms. As a result, the adaptability of the expert systems was improved. Nonetheless, they didn't suggest a hybrid and generalized solution to develop self-evolving expert systems. To this purpose, in this study, we propose an automatic knowledge acquisition and composite inference mechanism based on DM, FNN, and RDB-driven inference. Our proposed mechanism has five advantages empirically. First, it could extract and reduce the specific domain knowledge from incomplete database by using data mining algorithm. Second, our proposed mechanism could manipulate the ambiguousness in knowledge by using fuzzy membership functions. Third, it could construct the relational knowledge base and expand the knowledge base unlimitedly with RDBMS (relational database management systems). Fourth, our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy logic. Fifth, RDB-driven forward and backward inference is faster than the traditional text-oriented inference.

• International Commerce and Information Review
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• v.9 no.4
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• pp.3-19
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• 2007

The purpose of this paper is to study the pattern of interactive learning in strategic networks. Interactive learning is defined as the exchange and sharing of knowledge resources conducive to innovation between an innovator firm, its suppliers, and/or its customers. The strength of internal knowledge resources can either hamper or facilitate levels of interactive learning. We assume that more complex innovative activities urge firms to co-ordinate and exchange information between users and producers, which implies a higher level of interactive learning. To test our theoretical claims, we estimated the level of interactive learning of firms in strategic networks with: (1) their customers, (2) their suppliers. Theses analyses allow a comparison of the antecedents of interactive learning of firms participating in strategic networks. Our findings suggest that interactive learning with customers is positively affected by company's capabilities and value-created activities, and with supplies is positively affected by value-created activities and technology innovation centers.