摘要翻译：
为了更好地解释和分析使用模式，一个有用的步骤是形式化用户在Web中访问的资源的语义。本文针对这一问题，提出了一种使用日志语义形式化的方法，为查询具有表达性的使用模式奠定了基础。本文还提出了一种基于语义和时态约束的查询应答方法，该方法可以有效地解决使用行为的日志表示模式中的查询应答问题。我们已经处理了从DBPedia和语义网狗粮的使用日志中提取的3万多个用户浏览会话。所有这些事件都使用各自的领域本体和正在访问的Web资源的RDF表示进行语义形式化。通过实验结果验证了该方法的有效性，并对用户浏览Web数据的方式进行了探索性分析。
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英文标题：
《Enabling Semantic Analysis of User Browsing Patterns in the Web of Data》
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作者：
Julia Hoxha, Martin Junghans and Sudhir Agarwal
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最新提交年份：
2012
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Human-Computer Interaction        人机交互
分类描述：Covers human factors, user interfaces, and collaborative computing. Roughly includes material in ACM Subject Classes H.1.2 and all of H.5, except for H.5.1, which is more likely to have Multimedia as the primary subject area.
包括人为因素、用户界面和协作计算。大致包括ACM学科课程H.1.2和所有H.5中的材料，除了H.5.1，它更有可能以多媒体作为主要学科领域。
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一级分类：Computer Science        计算机科学
二级分类：Information Retrieval        信息检索
分类描述：Covers indexing, dictionaries, retrieval, content and analysis. Roughly includes material in ACM Subject Classes H.3.0, H.3.1, H.3.2, H.3.3, and H.3.4.
涵盖索引，字典，检索，内容和分析。大致包括ACM主题课程H.3.0、H.3.1、H.3.2、H.3.3和H.3.4中的材料。
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英文摘要：
  A useful step towards better interpretation and analysis of the usage patterns is to formalize the semantics of the resources that users are accessing in the Web. We focus on this problem and present an approach for the semantic formalization of usage logs, which lays the basis for eective techniques of querying expressive usage patterns. We also present a query answering approach, which is useful to nd in the logs expressive patterns of usage behavior via formulation of semantic and temporal-based constraints. We have processed over 30 thousand user browsing sessions extracted from usage logs of DBPedia and Semantic Web Dog Food. All these events are formalized semantically using respective domain ontologies and RDF representations of the Web resources being accessed. We show the eectiveness of our approach through experimental results, providing in this way an exploratory analysis of the way users browse theWeb of Data.
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PDF链接：
https://arxiv.org/pdf/1204.2713