以彈性的方式算網頁服務的相似度

Abstract

為了符合使用者的需求，網頁服務組成是在服務導向演算法中的一個重要層面。在這當中，服務相配是服務組合中一個不可或缺的部分。可是在做服務間彼此相配時，為了能夠達到一定的精準度與效率，是否能夠在形容服務的文件裡找出什麼是重要或關鍵的功能是必須的。透過隱含狄利克雷分布(LDA)模型來取出標題導向的做法，我們用一個類似層級分析法(AHP)的架構，定出一定數目的主題來當作條件並以文件的關鍵詞來當替換選項。從一個訓練好的LDA模型我們可以取得主題的機率分佈，並透過模糊隸屬函數來算出每個主題的權重。文件的關鍵詞在每個主題裡的重要性也是透過相似的方法找出來的。透過語言量化(linguistic quantifier)把有序加權平均(OWA)加到AHP的架構裡之後，就可以找出每一個關鍵詞在文章裡的重要值，也因此建立關鍵詞所代表的部件的重要性。

Web service composition is an important aspect of service-oriented computing (SOC) for fulfilling users' requests. As such, service matching plays an indispensable role in the process of composing services. However, in order to accurately and efficiently match services with one another, it is essential to be able to discern the important components of the service to be matched within its documentation. By taking a topic-oriented approach through the use of the Latent Dirichlet Allocation (LDA) model, we follow a framework similar to those of Analytic Hierarchy Process (AHP), with a set number of topics acting as the criteria and the document's keywords as the alternatives. In using a trained LDA model, a probability distribution of topics can be attained, used to calculate the weight of each topic through a membership transformation function. Keyword importance under each topic is also found using a similar process, and by incorporating Ordered Weight Averaging (OWA) into AHP through the use of linguistic quantifiers, an importance value for each keyword within the document can be found, and thus establishing the importance of the component the keyword represents.