Browsing by Author "Ke, Chih-Kun"

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CONTEXT-BASED KNOWLEDGE SUPPORT FOR PROBLEM-SOLVING BY RULE-INFERENCE AND CASE-BASED REASONING
(2011-07-01)
Ke, Chih-Kun
;
Liu, Duen-Ren
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Problem-solving is an important process that enables corporations to create competitive business advantages. Traditionally, case-based reasoning techniques have been widely used to help workers solve problems. However, conventional approaches focus on identifying similar problems without exploring relevant context of problem situations. Situation features are generally occurred according to the context characteristics of problems. Moreover, situation features collected are usually partial or incomplete. Workers need to use knowledge inferred from relevant context information and previous problem-solving experience to clarify the causes and take appropriate action effectively. In this paper, we propose to use rule inference to infer possible situation features based on context information. Association rule mining is used to discover context-based inference rules from historical problem-solving logs. The discovered patterns identify frequent associations between context information and situation features, and therefore, can be used to infer more situation features. By considering the inferred situation features, case-based reasoning can then be employed to identify similar situations effectively. Moreover, we employ information retrieval techniques to extract context-based situation profiles to model workers' information needs when handling problem situations in certain context. Effective knowledge support can thus be facilitated by providing workers with situation-relevant information based on the profiles. We develop a prototype system to demonstrate the effectiveness of providing context-based relevant information and decision-making knowledge to help workers solve problems.
Context-based knowledge support for problem-solving by rule-inference and case-based reasoning
(2008)
Liu, Duen-Ren
;
Ke, Chih-Kun
;
Wu, Mei-Yu
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Problem-solving is an important process that enables corporations to create competitive business advantages. Traditionally, case-based reasoning techniques have been widely used to help workers solve problems. However, conventional approaches focus on identifying similar problems without exploring relevant context of problem situations. Situation features are generally occurred according to the context characteristics of problem. Moreover, situation features collected are usually partial or incomplete. Workers need to use knowledge inferred from relevant context information and previous problem-solving experience to clarify the causes and take appropriate action effectively. In this paper, we propose to use rule inference to infer possible situation features based on context information. Association rule mining is used to discover context-based inference rules from historical problem-solving logs. The discovered patterns identify frequent associations between context information and situation features, and therefore can be used to infer more situation features. By considering the inferred situation features, case-based reasoning can then be employed to identify similar situations effectively. Moreover, we employ information retrieval techniques to extract context-based situation profiles to model workers' information needs when handling problem situations in certain context. Effective knowledge support can thus be facilitated by providing workers with situ ation-releva nt information based on the profiles.
Knowledge maps for composite e-services: A mining-based system platform coupling with recommendations
(2008-01-01)
Liu, Duen-Ren
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Ke, Chih-Kun
;
Lee, Jia-Yuan
;
Lee, Chun-Feng
;
資訊管理與財務金融系註：原資管所+財金所
Providing various c-services on the Internet by enterprises is an important trend in c-business. Composite e-services, which consist of various e-services provided by different e-service providers, are complex processes that require the cooperation among cross-organizational e-service providers. The flexibility and success of e-business depend on effective knowledge support to access related information resources of composite e-services. Thus, providing effective knowledge support for accessing composite e-services is a challenging task. This work proposes a knowledge map platform to provide an effective knowledge support for utilizing composite e-services. A data mining approach is applied to extract knowledge patterns from the usage records of composite e-services. Based on the mining result, topic maps are employed to construct the knowledge map. Meanwhile, the proposed knowledge map is integrated with recommendation capability to generate recommendations for composite e-services via data mining and collaborative filtering techniques. A prototype system is implemented to demonstrate the proposed platform. The proposed knowledge map enhanced with recommendation capability can provide users customized decision support to effectively utilize composite e-services. (c) 2006 Elsevier Ltd. All rights reserved.
Knowledge support for problem-solving in a production process: A hybrid of knowledge discovery and case-based reasoning
(2007-07-01)
Liu, Duen-Ren
;
Ke, Chih-Kun
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Problem-solving is an important process that enables corporations to create competitive business advantages. Traditionally, case-based reasoning techniques have been widely used to help workers solve problems. However, conventional approaches focus on identifying similar problems without exploring the information needs of workers during the problem-solving process. Such processes are usually knowledge intensive tasks; therefore, workers need effective knowledge support that gives them the information necessary to identify the causes of a problem and enables them to take appropriate action to resolve the situation. In this paper, we propose a mining-based knowledge support system for problem-solving. In addition to adopting case-based reasoning to identify similar situations and the action taken to solve them, the proposed system employs text mining (information retrieval) techniques to extract the key concepts of situations and actions. These concepts form profiles that model workers' information needs when handling problems. Effective knowledge support can thus be facilitated by providing workers with situation/action-relevant information based on the profiles. Moreover, association rule mining is used to discover hidden knowledge patterns from historical problem-solving logs. The discovered patterns identify frequent associations between situations and actions, and can therefore provide decision-making knowledge, i.e., appropriate actions for handling specific situations. We develop a prototype system to demonstrate the effectiveness of providing situation/action relevant information and decision-making knowledge to help workers solve problems. (c) 2006 Published by Elsevier Ltd.