在本文中，您复习了XML在数据挖掘中的用途和作用，包括模式匹配、变化监测、相似度搜索和监测、数据注释和语义。
In this article, you reviewed the use and roles of XML in data mining, including pattern matching, change detection, similarity search and detection, data annotation, and semantics.
从这个出发点来看，它使用的是文本挖掘，试图分析网页并鉴定他们关键的语义概念。
For this purpose it uses text mining to analyze Web pages and identify their key semantic concepts.
关联数据、语义分析、分析数据挖掘，这些都可以作为下一代网络产品和其它附加值的基础。
Linked data, semantic analysis, analytics and data mining all form a layer on top of the content-web that could serve as the foundation for the next series of applications and other added value.
这一事实导致数据挖掘机制对正在被处理的数据隐含了一些语义，以便定义一个有用的数据模型。
This fact leads data-mining mechanisms to imply semantics to the data being processed to define a useful data model.
这些相互关系需要进行挖掘，因为XML模式的语义通常很差，并且需要更多的人类参与才能推断出类似的事实。
These interactions are being explored because XML Schema often has poor semantics and requires more human interactions to deduce similar facts.
在已有的英语语义词典及大量训练集的基础上，应用机器学习、数据挖掘等技术进行知识获取并最终形成若干个概念推理网。
Machine learning and data mining techniques are applied to acquire knowledge and build a concept reasoning network based on semantic dictionary and large training set.
提出一种基于语义极性分析的餐馆评论挖掘方法。
This paper presents a method for restaurant reviews mining based on semantic polarity analysis.
传统的文本挖掘方法由于不能有效运用语义信息而难以达到更高的准确度。
However, conventional text mining technology cannot achieve high accuracy, because it cannot effectively make use of the semantic information of the text.
这种图形符号的语义联想方法可以用来改善信息检索系统的人机交互效率以及用于数据挖掘领域中的信息可视化技术。
The method of icon semantics association can be applied to the improving of the information retrieve system based on icon, and can be applied to information visualization technology in data mining.
用户不再仅仅满足获取直接信息，而需要获得更多的隐含语义信息，数据挖掘正是为了满足这一需求诞生的。
Users no longer meet the direct access to information only, and need to get more implied semantic information. data mining comes out for this.
基于本体的关联规则挖掘，是利用构建好的领域本体，结合数据挖掘算法，产生出具有语义的更符合用户需求的关联规则。
Mining association rules with ontological information bases on built domain ontology, using an algorithm of data mining, to produce semantic association rules which are more greatly satisfying users.
本文根据不同类型观点语句的特征，采用相应的意见挖掘技术分步进行语义极性分析。
According to the characteristics of different types of opinion sentences, we use appropriate opinion mining technology to cope with problem of semantic polarity.
本项目研究XML文档管理的若干关键技术，包括XML文档的存储与查询，XML数据的语义，针对XML数据的知识挖掘等。
This project studied the key technologies for the management of XML documents, including the storage and query of XML documents, the semantics of XML documents, and the knowledge mining, etc.
语义网络数据挖掘是基于语义网络环境的数据挖掘，它给数据挖掘技术的应用研究提出了新的课题。
Semantic Web data mining is a data mining area based on semantic Web, which introduce new challenges to data mining research.
文本对象词的字面关系和潜在语义关系很难被挖掘出来。
However the literal meanings of text object and potential semantic relations could hardly be excavated.
翻译实践中如何透过这些动物名词的字面语义信息，深入挖掘其中蕴藏的文化内涵并采用相应的翻译对策是一个值得研究的话题。
How to decode the cultural connotation of animal names apart from their literal meaning and using corresponding techniques in translation practice is a topic worth studying.
如何挖掘基于语义的相关模型是当前自动图像标注技术中一项重要而迫切的研究课题。
A popular technology is focused on how to build the semantic relevance model for the task of automatic image annotation.
系统在语义分析模块中利用语义推理进行检索词的规范和扩展，在语义检索模块通过语义推理挖掘关联隐含知识。
By using semantic inference this prototype system processes the specification and expansion of search keyword in the semantic analysis module and discovers the implicit knowledge.
实验表明，该算法挖掘了超链接间的潜在语义关系，能有效的引导主题挖掘。
Experiment results show that WHITS focuses on mining the potentially semantic relationship between hyperlinks and performs quite well in the topic-specific crawling.
我们介绍了一种新的概念化框架，语义图像挖掘，使得研究者能够在网络数据分析中将图像挖掘和本体论推论结合起来。
We introduce a new conceptual framework, semantic graph mining, to enable researchers to integrate graph mining with ontology reasoning in network data analysis.
我们介绍了一种新的概念化框架，语义图像挖掘，使得研究者能够在网络数据分析中将图像挖掘和本体论推论结合起来。
We introduce a new conceptual framework, semantic graph mining, to enable researchers to integrate graph mining with ontology reasoning in network data analysis.
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