許多企業將客服部門常見的問題與解答建立知識庫，以利知識再利用，但是知識的分類方式卻是以人工判斷為依據，此方法可能造成歸類不一的情形。本研究以個案S公司之線上問與答系統為研究對象，建立一套軟體領域之FAQ自動分類系統協助其常見問題的分類。 本研究採用系統發展研究法，首先建立FAQ自動分類系統之領域本體論，設計分類方法與規則讓系統能夠自動產生類別，接著實際建置FAQ自動分類系統，並透過Ｓ公司的客服人員訪談與使用者滿意度調查來驗證系統的可行性及搜尋效率。研究結果發現，FAQ自動分類系統能夠自動產生與資料相符的類別，但是仍需要人工適時調整分類，以達較佳的分類效能。在經過訪談與滿意度調查後發現，使用問題屬性對資料進行分類比使用軟體名稱分類的搜尋效率高，雖然使用者對於FAQ自動分類系統與個案S公司原本的FAQ系統的滿意度皆表示滿意，但在問題搜尋效率上，本研究所建構的FAQ自動分類系統之搜尋速度明顯高於S公司原本的系統。 FAQ are often built up by the customer service department in enterprises as a knowledge base to facilitate the reusability of solution for frequent encountered problems. However, subjective judgments might cause inconsistent classification, which lead to inappropriate response to users'' inquiries, and also retard the searching efficiency. This research is taking on-line service FAQ of IT Company S as a model to study the FAQ Automatic Categorizing System, which is expected to offer a better searching quality to respond to users’ inquiries in the future.
First of all, the domain ontology for the case S is designed for the research, then, the classification method and rule is built to enable the automatic creation of classes by the system. After that, the System Development Research Methodology is used to develop the FAQ Automatic Categorizing System. Finally, interviews and surveys are conducted to explore the users'' satisfaction with the system, evaluate the feasibility, and investigate the searching efficiency of the system. The result of this research showed that FAQ Automatic Categorizing System is capable of creating the classes that matches the FAQ data automatically with timely adjustment by manual to show better performance. After the investigation, the result shows that it has higher efficiency to search the FAQ using the question attribute than using the software name. Although the original system S company using has the same satisfaction to that of the FAQ Automatic Categorizing System, but the FAQ Automatic Categorizing System stands out when doing the FAQ search. It was expected to regard FAQ Automatic Categorizing System as a model for the future research and practice.