以知識本體建構之網路服務模糊搜尋

Abstract

回顧過去幾年，網際網路從無到有，這其中最重要原因不外乎其簡單與無所不在的特性，而Web Service的興起更是將網路的應用帶向另一個紀元，因為Web不僅可以作為一個資訊平台，也將成為一個服務平台。而若想發揮網路服務的強大應用能力，關鍵就是如何正確快速地匹配網路服務。為了改善網路服務的搜尋效能，目前提供的匹配機制有UDDI、LARKS和CBR。但目前的匹配機制並沒有將服務的內容作為匹配的考量之ㄧ，且UDDI也無法透過模糊語意進行搜尋。因此，本研究將服務的內容也納入網路服務搜尋的考量中，用模糊邏輯和知識本體來展現服務內容。實驗證明，本架構能讓使用者使用自然的語句，較準確地篩選出適合的網路服務。

To fully realize the potential of Web services it is necessary to develop viable dynamic discovery and composition techniques. Matchmaking is considered as one of the crucial factors to ensure dynamic discovery and composition of Web services. Current matchmaking methods such as UDDI or LARKS are inadequate given their inability to abstract and classify Web services on the capability of services, software signatures, and so on. Current mechanisms have lost a hidden dimension for matchmaking. That is, the underlying data of Web services. Otherwise, UDDI doesn’t support the imprecise query. Therefore, our research proposes a novel framework which exploits fuzzy logic in order to abstract and classify the underlying data of Web services as fuzzy terms and rules. The aim is to allow vague terms in the search query and to provide more suited services to requesters.