semantic attributes 中文意思是什麼
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Meanwhile, it supports the international open specification. owl - s it based owl is the primary production for the describing model of semantic web - service in international, it is a mark model for describing web service, it provide a mark symbols set that is explicable and precise to describe the attributes and functions of web service for computer. owl - s uses serviceprofile 、 servicemodel and servicegrounding to describe “ what the service does ? ” 、 “ how it works ? ” 、 “ how to access it ? ” this paper provides an introduction to the basic concepts and architecture of soa ( service - oriented architecture ), the features of web service technology, anddiscusses the relationship between soa and web services
而owl框架下的owl - s是國際上語義web服務描述模型方面的主要研究成果，它是一種描述web服務的標記模型，為機器提供了可解釋的、精確的、關于web服務屬性和能力描述的一系列標記符。它是基於owl語言為描述web服務而定義的一個本體，主要通過服務proile （ serviceprofile ） ，服務模型（ servicemodel ）和服務綁定（ servicegrounding ）三個類來描述服務做什麼、服務如何做、服務如何訪問等三方面的語義，從而允許服務的自動發現、執行、組合和運行的監視。 -
3 ) semantic classification model based som network we use the classification model to combines attributes within a database. this is done using an unsupervised learning algorithm. the output is used as training data for the next stage
3 ）基於som網路的語義分類模型設計建立som網路模型，將元數據特徵向量進行分類，形成bp網路的目標向量，用於匹配規則的提取。 -
In this paper we use the color auto - correlogram as the similarity metrics of images in low - level feature space, and change the bandwidth function. then we propose the semantic relevance feedback. the system react differently to the positive and negative user ' s feedback so that the system can go on learning after the annotation process by updating the probabilities of the list of attributes of the relevant images and reaching the real values
本文引入顏色自相關圖特徵作為圖像在底層特徵空間相鄰的度量，並修改了框架中帶寬的計算函數，然後引入反饋機制，對于用戶的正反饋和負反饋分別作不同的處理，以便在使用過程中，系統能夠繼續學習，根據反饋更新圖像的概率鏈表，使之逐漸接近真實情況。 -
4 ) semantic discovering and matching model based bp network the classifier output is used as training data for a bp neural net. the net produced by this can recognize attributes within the database based on their metadata and emerge learning rules
4 ）基於bp網路的語義發現和匹配模型設計建立bp網路模型，通過對樣本數據進行學習進而形成匹配規則，用於異構數據庫之間的語義匹配。 -
2 ) semantic description methods based on metadata we construct a semantic model of metadata to describe attributes characters. the model includes data schema and data content statistic
2 ）基於元數據的語義描述方法以數據庫元數據為基礎，建立數學模型，用於描述數據庫對象的語義特徵，涉及范圍包括數據模式和數據內容統計。 -
Recent proposal have suggested web usage mining as an enabling mechanism to overcome the problem associated with more traditional web personalization techniques such as collaborative or content - based filtering. these problems include lack of scalability. reliance on subjective user ratings or static profiles, and the inability to capture a richer set of semantic relationships among objects. yet, usage - based personalization can be problematic when little usage data is available pertaining to some objects or when the site content changes regularly. for more effective personalization, both usage and content attributes of a site should be integrated into a web minging framwork and used by the recommendation engine in a uniform manner
為解決傳統技術中出現的這些問題，一些研究提出將web使用日誌的挖掘應用到個人化技術中。 web使用記錄的挖掘雖然有諸多的優點，卻不能適應用戶的使用信息較難獲取及站點內容經常變化的情況。為了使個人化系統更有效，我們需要將web使用記錄的挖掘與web內容挖掘集成到同一個結構中，由推薦引擎以統一的方式使用他們。 -
The elementary research into the semantic attributes of english idioms and their translation
英文習語的語義特徵與中英對譯之探索 -
In order to calculate the semantic coupling effectively, the edge counting method is revisited for measuring basic semanticsimilarity by considering the weighting attributes from where theyaffect an edge s strength. the attributes of scaling depth effect, semantic relation type, and virtual connection for the edge counting areconsidered. furthermore, how the proposed edge counting method could bewell adapted for calculating context - based similarity is showed. thorough experimental results are provided for both edge counting andcontext - based similarity
為有效地計算語義耦合值，我們對度量語義基本相似度的邊計算edge counting方法進行了修改，採用加權的屬性值來修正連接兩個概念之間的邊的強度所考慮的屬性包括：縮放深度效果scaling depth effect語義關系類型semantic relation type虛擬連接virtual connection等。 -
However, when you have xml documents with hundreds or thousands of records, and each element can have several attributes all of which can appear in any order, semantic equivalence is harder to determine
但是，如果xml文檔有成百上千條記錄，每個元素都有多個屬性（都能以任意的順序出現） ，就很難判定語義上的等價性。
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