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.
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。
This architecture aligns with W3C web standards for the semantic web, and allows much more flexible searching and data mining than would be possible with a MARC record.
这个结构和W3C的语义网网络标准相吻合，而且相比MARC数据，它能够进行更加灵活的搜索和数据收割。
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.
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。
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.
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。
Users no longer meet the direct access to information only, and need to get more implied semantic information. data mining comes out for this.
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。
Semantic Web data mining is a data mining area based on semantic Web, which introduce new challenges to data mining research.
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。
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.
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。
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