Knowledge Representation via Knowledge Vocalization--A Case Study on Equipment User Manuals

• Main Authors: Lee Jing-Wen, 李靜玟
• Other Authors: Hou Jiang-Liang
• Format: Others
• Language: zh-TW
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/14169045862391646238

碩士 === 國立清華大學 === 工業工程與工程管理學系 === 97 === In the shop floor of a factory, operators have to use machines to perform manufacturing processes. Usually the operation status of machines can be identified via sounds. In the equipment user manuals, knowledge is usually represented via texts or illustrations and knowledge receivers might spend much time to recognize the text-based sound expressions. Thus, a vocalization representation scheme for the text-based sound expressions can assist knowledge receivers to efficiently and effectively recognize this type of knowledge. This research aims at developing a knowledge vocalization methodology in order to convert the knowledge contents with text-based sound expressions into the vocalized expressions. The proposed methodology consists of three modules namely Sound Expression Identification (SEI), Target Sentence Extraction and Formatting (TSEF) and Knowledge Content Vocalization (KCV). In the SEI module, the components with sound expressions are identified from the sentences. Based on the identified sound components, the target sentences with sound expressions are extracted from the free-form documents and expressed as formatted matrices via the TSEF module. In the KCV module, all text-based, formatted sound expressions are represented via vocalized expressions. As the knowledge contents with sound expressions can be represented via knowledge vocalization, knowledge receivers can efficiently recognize the knowledge contents and knowledge reuse can be facilitated. Moreover, based on the proposed methodology, a Web-based prototype system for vocalized knowledge sharing is also developed and the equipment manuals are employed to evaluate the feasibility and performance of the proposed methodology. As a whole, this research provides a knowledge representation and vocalization model to facilitate knowledge receivers to efficiently and accurately acquire the knowledge contents with sound expressions.