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Dynamic, Behavior-Based User Profiling Using Semantic Web Technologies in a Big Data Context

机译:基于行为的基于行为的用户分析在大数据上下文中使用语义Web技术

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The success of shaping the e-society is crucially dependent on how well technology adapts to the needs of each single user. A thorough understanding of one's personality, interests, and social connections facilitate the integration of ICT solutions into one's everyday life. The MindMinings project aims to build an advanced user profile, based on the automatic processing of a user's navigation traces on the Web. Given the various needs underpinned by our goal (e.g. integration of heterogeneous sources and automatic content extraction), we have selected Semantic Web technologies for their capacity to deliver machine-processable information. Indeed, we have to deal with web-based information known to be highly heterogeneous. Using descriptive languages such as OWL for managing the information contained in Web documents, we allow an automatic analysis, processing and exploitation of the related knowledge. Moreover, we use semantic technology in addition to machine learning techniques, in order to build a very expressive user profile model, including not only isolated "drops" of information, but inter-connected and machine-interpretable information. All developed methods are applied to a concrete industrial need: the analysis of user navigation on the Web to deduct patterns for content recommendation.
机译:塑造电子社会的成功是至关重要的,这依赖于技术适应每个单一用户的需求。彻底了解一个人的个性,利益和社会联系,促进了ICT解决方案将ICT解决方案的整合到一个人的日常生活中。 MindMinings项目旨在基于用户在Web上的自动处理的自动处理来构建高级用户配置文件。鉴于我们的目标(例如,异构来源和自动内容提取的集成)提供了各种需求,我们选择了各种网络技术,以实现其提供机器可处理信息的能力。实际上,我们必须处理已知的基于网络的信息是高度异构的。使用诸如owl的描述性语言来管理Web文档中包含的信息,我们允许自动分析,处理和利用相关知识。此外,我们除了机器学习技术之外,我们还使用语义技术,以便构建一个非常富有表现力的用户简档模型,包括不仅隔离的信息的“滴”,而且包括连接和机器可解释的信息。所有开发的方法都适用于具体工业需求:分析网上的用户导航,以扣除内容推荐模式。

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