图 书 馆
表征搜索查询术语的语义相关性Characterizing Semantic Relatedness of Search Query Terms |
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| 课程网址: | http://videolectures.net/ecmlpkdd09_benz_csrsqt/ |
| 主讲教师: | Dominik Benz |
| 开课单位: | 卡塞尔大学 |
| 开课时间: | 2009-10-20 |
| 课程语种: | 英语 |
| 中文简介: |  在搜索引擎查询日志中挖掘语义信息对于优化搜索引擎和引导语义Web应用程序都具有巨大潜力。最近，用户与搜索引擎的交互（更具体的点击日志信息）被视为通过查询术语隐式标记资源。由此产生的结构（以前称为logsonomy）表现出与民俗分类的结构相似性，分词分类在社会书签系统中使用自由选择的关键词注释资源的显式过程中演变。对于folksonomy案例，适当的相关性度量已经证明能够收集用户，标签和资源三方图中固有的新兴语义。由于报告的结构相似性，在这项工作中，我们将这种方法扩展到logsonomies。更具体地说，我们将几个与查询术语相关的度量应用于logsonomy图，并通过将其与基于WordNet的用户验证的相关性度量相对应，为每个度量提供语义表征。将结果与分析folksonomy数据的先前结果进行比较，我们发现logsonomies中的日志数据的形式化保留了语义信息。我们应用的一些相关性度量证明能够捕获这些紧急语义，类似于民俗案例，而其他相关性则表现出不同的特征。通过这种方式，我们提供了一种新颖而系统的方法来比较用户交互的紧急语义与搜索引擎和社交书签系统。我们得出结论，两种新兴结构中固有的语义信息类型是相似的，并为选择适当的查询项关联度量提供了选择。 |
| 课程简介: | Mining for semantic information in search engine query logs bears great potential for both the optimization of search engines and bootstrapping Semantic Web applications. The interaction of a user with a search engine (more specifi cally clicklog information) has recently been viewed as implicit tagging of resources by query terms. The resulting structure, previously called a logsonomy, exhibits structural similarities to folksonomies, which evolve during the explicit process of annotating resources with freely chosen keywords in social bookmarking systems. For the folksonomy case, appropriate measures of relatedness have shown to be capable to harvest the emerging semantics inherent in the tripartite graph of users, tags and resources. Motivated by the reported structural similarities, in this work we extend this methodology to logsonomies. More specifi cally, we apply several measures of query term relatedness to the logsonomy graph and provide a semantic characterization for each measure by grounding it against user-validated relatedness measures based on WordNet. Comparing the outcome with prior results of analyzing folksonomy data we nd that the formalization of log data in logsonomies retains the semantic information. Some relatedness measures we applied prove to be able to capture these emergent semantics similarly to the folksonomy case, while others exhibit di fferent characteristics. In this way we provide a novel and systematic approach to compare the emergent semantics of user interactions with search engines and social bookmarking systems. We conclude that the type of semantic information inherent in both emerging structures is similar, and inform the choice of an appropriate measure of query term relatedness for a given task. |
| 关 键 词: | 搜索引擎; 语义信息; 分词分类 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-03-23：lxf |
| 阅读次数: | 112 |