• Knowledge Management Research
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• v.15 no.4
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• pp.189-206
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• 2014

Knowledge management is considered an important factor for competitive advantage and sustainability for firms. As many knowledge management systems failed to achieve the desired results, enterprise social media (ESM) has received considerable attention as an alternative solution for knowledge sharing within a firm. This paper attempts to investigate the influence of various aspects of ESM on knowledge sharing. While previous literature mainly focused on structural aspects of ESM, this study focuses on social psychological aspects, such as social connectedness, social awareness, and social presence, along with reputational aspects (such as self-presentation). Further, in order to clarify knowledge sharing behavior, this study classifies knowledge sharing behavior into two categories, knowledge contribution and knowledge acquisition. The data used in this study was collected from 179 individuals who have experience in their own ESM. The results show that both social connectedness and self-presentation positively influence the two types of knowledge sharing behavior, i.e., acquisition and contribution. Meanwhile, social awareness turns out to be a significant determinant of knowledge contribution only. Contrary to our expectations, however, social presence does not significantly influence knowledge sharing behavior.

• Journal of Intelligence and Information Systems
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• v.1 no.1
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• pp.91-109
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• 1995

This paper compared four knowledge acquisition methods (namely, neural network, case-based reasoning, discriminant analysis, and covariance structure modeling) for allergic rhinitis. The data were collected from 444 patients with suspected allergic rhinitis who visited the Otorlaryngology Deduring 1991-1993. Among four knowledge acquisition methods, the discriminant model had the best overall diagnostic capability (78%) and the neural network had slightly lower rate(76%). This may be explained by the fact that neural network is essentially non-linear discriminant model. The discriminant model was also most accurate in predicting allergic rhinitis (88%). On the other hand, the CSM had the lowest overall accuracy rate (44%) perhaps due to smaller input data set. However, it was most accuate in predicting non-allergic rhinitis (82%).

• Journal of Intelligence and Information Systems
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• v.14 no.1
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• pp.67-84
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• 2008

This paper proposes a design and realization for the semantic search system. The proposed model includes three Architecture Layers of a Semantic Search System ; (they are conceptually named as) the Knowledge Acquisition, the Knowledge Representation and the Knowledge Utilization. Each of these three Layers are designed to interactively work together, so as to maximize the users' information needs. The Knowledge Acquisition Layer includes index and storage of Semantic Metadata from various source of web contents(eg : text, image, multimedia and so on). The Knowledge Representation Layer includes the ontology schema and instance, through the process of semantic search by ontology based query expansion. Finally, the Knowledge Utilization Layer includes the users to search query intuitively, and get its results without the users'knowledge of semantic web language or ontology. So far as the design and the realization of the semantic search site is concerned, the proposedsemantic search system will offer useful implications to the researchers and practitioners so as to improve the research level to the commercial use.

• 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.

• Journal of the Korean Society for information Management
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• v.16 no.4
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• pp.53-74
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• 1999

This paper describes concept mapping techniques for eliciting and representing knowledge. Concept mapping techniques range from very informal to very formal. Informal concept mapping techniques are usually very easy to use and understand for humans, but not for computers. Formal concept mapping techniques are computational, but humans usually find them hard to understand and use. A knowledge acquisition and representation tools which handle both kinds, and the transition from informal to formal, would be very useful. It is proposed that concept maps be regarded as basic components of any knowledge-based system, complementing text and image with formal and informl active diagrams.

• Knowledge Management Research
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• v.17 no.1
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• pp.135-154
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• 2016

Absorptive capacity has been increasingly thought of as a potential source of innovation. From the knowledge management perspective, absorptive capacity is composed of a set of activities dealing with acquisition, assimilation, transformation, and exploitation of external and internal knowledge. This study investigates what relationship the absorptive capacity of individuals who have technical knowledge in the organization has with process innovation and product innovation. Mobile based survey was conducted from the employees working for the largest electronics manufacturer in Korea. The analyzed data was based on 156 responses from 199 participants. The analysis result shows that four phases of absorptive capacity such as acquisition, assimilation, transformation and exploitation have different effects on process innovation and product innovation, respectively. Specifically, transformation is found to be most critical in leading to innovation.

• Proceedings of the CALSEC Conference
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• 2005.03a
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• pp.55-61
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• 2005

We will introduce a new web-based knowledge management system in progress, in which XML-based web information extraction and our structuring knowledge technologies are combined using ontology-based natural language processing. Our aim is to provide efficient access to heterogeneous information on the web, enabling users to use a wide range of textual and non textual resources, such as newspapers and databases, effortlessly to accelerate knowledge acquisition from such knowledge sources. In order to achieve the efficient knowledge management, we propose at first an XML-based Web information extraction which contains a sophisticated control language to extract data from Web pages. With using standard XML Technologies in the system, our approach can make extracting information easy because of a) detaching rules from processing, b) restricting target for processing, c) Interactive operations for developing extracting rules. Then we propose a structuring knowledge system which includes, 1) automatic term recognition, 2) domain oriented automatic term clustering, 3) similarity-based document retrieval, 4) real-time document clustering, and 5) visualization. The system supports integrating different types of databases (textual and non textual) and retrieving different types of information simultaneously. Through further explanation to the specification and the implementation technique of the system, we will demonstrate how the system can accelerate knowledge acquisition on the Web even for novice users of the field.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.459-460
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• 2015

There has been highly increased interests in serious games as a result of the development of game industry. But there are limitations in further development due to negative aspects of game in spite of positive aspects of serious game. Recently, related studies have made efforts to provide positive outcomes of serious games. This study examine to analyze the impact of knowledge acquisition among the various outcome variables in serious games. Through the study, we examine the literature review about knowledge acquisition in various domain on serious games and identify the methodology for assessing outcome variables especially knowledge acquisition.

• Journal of Intelligence and Information Systems
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• v.11 no.2
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• pp.151-173
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• 2005

We developed a methodology of rule acquisition from texts such as Web pages which utilizes ontology in identification of rule components. We expect that the proposed methodology can reduce the bottleneck of rule acquisition and contribute to the utilization of rule based systems. As parts of our research, we designed an ontology for rule acquisition named OntoRule and proposed a rule acquisition methodology through OntoXRML which is an acquisition tool using OntoRule. Also, we evaluated our approach by calculating missed recommendations and wrong recommendations of rule components in rule acquisition experiments over three online bookstores.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.147.1-147
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• 2001

Future robots, especially service and personal robots, will need much more intelligence, robustness and user-friendliness. The ability to learn contributes to these characteristics and is, therefore, becoming more and more important. Three of the numerous varieties of learning are discussed together with results of real-world experiments with three autonomous robots: (1) the acquisition of map knowledge by a mobile robot, allowing it to navigate in a network of corridors, (2) the acquisition of motion control knowledge by a calibration-free manipulator, allowing it to gain task-related experience and improve its manipulation skills while it is working, and (3) the ability to learn how to perform service tasks ...