On the basis of this work, we continue to carry on the research of Deep-seated Semantic Computing and really realize the understanding of the nature language with the Semantic Neural Network.
基于表层语义理解的工作，我们继续进行深层语义计算的研究，真正意义上的实现语义神经网络的自然语言理解。
In reality, the message exchanged in SOAP is just an XML document that contains the serialization information for computing without the semantic significance.
实际上，SOAP中交换的消息只是包含用于进行计算的序列化信息的XML文档，并没有语义方面的意义。
Documents similarity computing with attribute barycenter coordinate model is a relatively new method, but the semantic information easily loss and is inefficient.
基于属性的重心剖分模型是一种较为新颖的文档相似度计算模型，但容易导致语义信息丢失和效率低下。
The semantic relationships between multi-context key words are taken into account and the fuzzy similarity matrix is derived from non-distance computing in this method.
该方法不仅考虑了多背景关键词之间的语义关系，而且通过非距离计算得到模糊相似矩阵。
All kinds of approaches to computing the semantic matching degree on IFS, including those of near compactness, semantic distance, similarity and compound conditions match, are discussed.
讨论了计算直觉模糊集语义匹配度的若干途径，包括贴近度、语义距离、相似度、复合条件的匹配等。
Computing the semantic similarity of sentences by the method of cosine, eigenvalue come from the skeleton semantic clip, and the semantics of sentence expressed the vector space model.
用骨架语片做特征项，用空间向量模型表示文本语义，用语片的出现频度做语片权重，用余弦法计算文本间语义相似度。
Finally, research on these issues, designs and implements query processing system based on client-side semantic caching in mobile computing environment.
最后，通过对上述问题的研究，设计并实现移动计算环境下客户端语义缓存的查询系统。
Semantic caching is very attractive for use in mobile computing environments.
语义缓存是非常有吸引力的移动计算环境中使用。
A semantic security policy language, called SSPL, was proposed for distributed computing environment.
提出了一种用于分布式计算环境的语言安全策略语言sspl。
To solve these problems, computing entities need to understand and process information in the network, which is the support of service discovery on the "semantic" level.
解决上述问题，需要计算实体能够理解和处理网络中的信息，即需要“语义”层次上服务发现的支持。
The traditional methods measure semantic similarity by computing semantic distance between nodes, which are difficult to ensure the accuracy of computation.
传统的语义相似度计算方法大都利用结点间的语义距离来衡量，难以保证计算的准确性。
Domain knowledge, semantic integration method and proof combination method are applied in the model. Expression and computing methods of uncertain degree are proposed for every phase.
该模型运用领域知识、语义集成方法和证据组合方法处理模式集成各个阶段的不确定性，并给出了各阶段不确定度的表示和计算方法。
Based on the Context Framework, the se-mantic frame of text is designed and the algorithm of computing semantic frame is developed.
在语境框架的基础上，设计实现了文本相似度计算算法。
His research fields are Pervasive Computing, Psychophysiological Computing, Collaborative Work Technology and Semantic Web.
主要研究领域为普适计算、心理生理计算、协同工作技术和语义网。
His research fields are Pervasive Computing, Psychophysiological Computing, Collaborative Work Technology and Semantic Web.
主要研究领域为普适计算、心理生理计算、协同工作技术和语义网。
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