Building a knowledge-based model for project risk evaluation

• Main Authors: Yi-Fang Hsu, 許宜芳
• Other Authors: Yu-Liang Chi
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/cnnyts

碩士 === 中原大學 === 資訊管理研究所 === 98 === This study evaluated and illustrated the importance of project risk from the angle of project management. Because, in the past, managers’ experiences and subjective judgments were highly relied on, project costs were possibly over-estimated, and resources were not properly arranged. Quality and success of projects were therefore influenced. If in the initial stage of a project, possible risks which may arise in this stage can be predicted, plans to respond to and handle these risks can be made in time. And if potential risks which may arise during project implementation can be known in advance, immediate responses can be made and related measures can be taken. This way, quality and progress control of a project can be improved, helping to increase the successful rate of that project. This study aimed to use ontology with expert domains, according to different requirements of different project types, to find proper risk analysis techniques and solutions for subtasks of the requirements while considering life cycles as subtasks, in order to offer project managers more choices of solutions and help them to improve project quality. During implementation of a project, useful information and knowledge were retrieved. Evaluations were made with risk factors proposed by experts and scholars. Through historical information and project records, possibilities of risks were identified and analyzed. A knowledge model was developed through the processes of creation, integration, storage, and transferring, with the concepts of knowledge systems. The model was used to explore risk factors and corresponding solutions, so that knowledge could be shared and re-used. The example used in this study was a software project. By integrating the experts’ knowledge, the features of the project, and the risk factors, combining ontology with semantic rules, a knowledge model was built. Based on the historical project information, the inferences of potential risks of the current project, responses to the previous projects, and the levels of the risks were made, offering project managers a reference when supervising risks arising during implementation of a project.