| Summary: | 碩士 === 國立高雄第一科技大學 === 資訊管理所 === 93 === Financial distress forecasting is one of the important topics in analyzing the health condition of a company. The processes of its analysis depend on the expert's professional knowledge. However, the processes of analysis and diagnosis are generally influenced by expert's past experience, mental factor, physiological factor or subject cognition. Using expert systems to replicate the processes could avoid the influence of the artificial factor. Most expert systems are rule based systems, which use rule to express the content of knowledge base. One major drawback in using present expert system is the fact that once the knowledge content become huge and complex, it will become inefficient. Besides, each expert system uses own format to store their knowledge base, it is difficult to reuse and share knowledge base.
Such being the case, this research apply the methodology of ontology in the artificial intelligence to analyze the domain knowledge of financial distress forecasting, and use OWL (Web ontology language) which is recommended by W3C to build up our domain knowledge. We also use rules to express the finance distress forecasting processes which are implemented by JESS(Java Expert System ). Finally, we combine both to become one complete knowledge base, and built up a Forecasting Expert System .
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