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。
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