服務導向架構(Service Oriented Architecture)是一種基於應用程式服務架構的軟體概念，可提供更高彈性、更高效率、及資訊整合的IT環境。網際服務(Web Services)是一種在SOA的概念下，由業界所共同遵守的分散式運算單元標準。透過網際服務可實現重用性(reuse)高、鬆偶合式(loosely coupling)的軟體架構、跨平台且分散的運算、和動態的提供與取得服務等優點。網際服務的搜尋配對在開發SOA系統過程是最主要的一個關鍵。目前對網際服務的搜尋上主要是對UDDI(Universal Description ,Discovery and Integration)使用關鍵字與服務分類的方式從WSDL(Web services Description Language)去尋找符合的服務。然而WSDL包含的資訊有限，無法提供太多的資訊。所以近年來引進語意網(Semantic Web)的本體論(Ontology)並對網際服務做語意(Semantic)上的描述以加強網際服務的搜尋與組裝執行是一個研究議題。
許多基於網際服務語意描述搜尋的研究都在探討如何應用語意推理的方式加強搜尋的準確度，但都屬於靜態的搜尋演算法，不具自我調校的能力。本論文提出以自適應的投票決策機制搭配語意比對，而產生客製化且精確的語意搜尋演算法。自適應的投票決策機制的主要概念是採用進化計算(Evolutionary Computation)做為學習演算法並藉由類神經網路(Neural Network)模擬使用者決策行為，最後再採用投票機制加強準確率。藉由自適應的投票決策機制能使搜尋系統可以學習使用者在選擇搜尋結果時是如何做決策，而做自我調校，所以能夠回傳給使用者少量且準確的資訊。

Service-oriented architecture (SOA) is a concept of program architecture, can offer flexible, and efficient, and IT environment that provides integrated information. The web service is basing on the concept of SOA, is a standardized, distributed computing component which has been adopted in industry. The utilization of web services can achieve the software with the advantage of highly reusable, loosely coupling software architecture, distributed computing and dynamic service providing on different platforms. Service-matchmaking component is one of the important keys for SOA system development. Current service-matchmaking methods mainly use the method of key words and service category search in an UDDI (Universal Description, Discovery and Integration) for the suitable WSDL. However , since the information of WSDL is so limited, it is unable to get useful information from WSDL. Therefore, one solution is to introduce the ontology of semantic web to enhance web services description.
Many researches emphasized the accuracy of service-searching based on the reasoning of description logic’s, but most issues are subject to calculating under static searching mode which doesn't involve the ability of self-adoption. The context of this thesis combines self-adapted decision-making mechanism of voting with description logic’s (DL) subsumption reasoning. It could produce customized and precise search algorithm. The main idea of this self-adapted voting decision-making mechanism is based on the application of evolution computation as the learning algorithm; then to do the simulation by means of neural network to perform the decision behavior of the end-user. Afterwards the compliance is checked by a voting mechanism to increase its accuracy. The searching system will automatically simulate the end-users within the criteria of how to perform the decision-making which involves the ability of self-auditing. Therefore it could be responded and send feedback to the end-user with enough and accurate information.

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