CAKE - Collaborative Acquisition for Knowledge Evolution

• Main Authors: Chia-Wen Teng, 鄧嘉文
• Other Authors: Shian-Shyong Tseng
• Format: Others
• Language: en_US
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/18979797970029716126

碩士 === 國立交通大學 === 資訊科學與工程研究所 === 94 === Due to the knowledge explosion, the knowledge can be classified into static knowledge and dynamic knowledge. Although many knowledge acquisition methodologies have been proposed to systematically elicit rules of static knowledge from domain experts, none of these methods discusses the issue of discovering dynamic knowledge due to the lack of sufficient context information. In this thesis, we will propose a new collaborative knowledge acquisition methodology, Collaborative Acquisition for Knowledge Evolution (CAKE), to solve the issue of discovering dynamic knowledge by collecting sufficient relevant context information to help experts notice the occurrence of dynamic object. First, we define static profiles and dynamic behaviors as the context information to assist experts to be aware of the occurrence of dynamic knowledge based on several collaborative heuristics for service-sensitive and symptom-similar events. Variant Objects Discovering Knowledge Acquisition (VODKA) and Trend Event Acquisition (TEA) are used to construct a new dynamic acquisition table to facilitate the acquisition of variant knowledge and to automatically adjust the relative importance of each attribute to each object in the attribute ordering table (AOT) to discover the evolutional knowledge in CAKE. This is useful to help experts understand the changing behaviors of attributes to each object. Furthermore, CAKE is designed to use Dynamic EMCUD, a new version of an existing knowledge acquisition system called EMCUD which relies on the repertory grids knowledge acquisition technique to manage object/ attribute-values tables and to produce inferences rules from these tables, to update existing tables by adding new objects or new object attributes in new acquisition for adapting the original embedded rules with the dynamic AOT. Besides, a Worm detection prototype system is implemented to evaluate the effectiveness of CAKE.