A Mechanism Combining CBR with Expert Finding for Problem Diagnosis Support

• Main Authors: Chang, Chen-Hao, 張宸豪
• Other Authors: Li, Yung-Ming
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/71888791582550971129

碩士 === 國立交通大學 === 管理學院資訊管理學程 === 100 === Nowadays advanced manufacturing and information technologies have impacted on every aspect of product development significantly. With the increasing of product complexity and globalization manufacturing, enterprise manufacturing information change more and more complex. Therefore, sharing manufacturing knowledge understanding such as personal experience and exception handling is one important issue to be solved. This paper introduces a research integrated text mining technique and top-k support documents expert search model based on Case-based reasoning (CBR) by using knowledge of the semantic structure of documents. CBR can use known experiences to solve new problems, we store the past problems as cases in a case base and a new case is classified by determining the most similar case from the case base. And the use of degree centrality of the expert candidate network can find the expert with the most influence through the experts score rank. Our approach of the CBR based expert recommends combined the reliability and influence between the experts can help users to get the expert support and resolve the potential problems of CBR such as lack of feedback and lack of a sufficiently rich case library. With these methodologies, we establish the Problem Support and Expert Recommend System (PSERS), which applied in the case base of fault diagnosis expert system of manufacturing information. From the experimental results, these techniques are shown to be very effective in the modeling and extraction of the domain knowledge in the case base.