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在RDF文档和社交网络中搜索和排名

Searching and ranking in RDF documents and social networks
课程网址: http://videolectures.net/semseach09_graves_srrdsn/  
主讲教师: Sibel Adalı, Konstantin Mertsalov, Alvaro Graves
开课单位: 伦斯勒理工学院
开课时间: 2009-05-27
课程语种: 英语
中文简介:
随着基于语义Web的应用程序越来越流行,非常大的RDF文档正变得越来越普遍。 SPARQL是查询RDF数据的事实上的标准,研究非常大的RDF图的SPARQL接口的有效实现近年来引起了很大的兴趣。但是,在大型数据集中,用户面临的问题是,查询的结果集可能很大。在这种情况下,用户无法从哪里开始查看结果,因为所有这些都是同等有效的。此外,鉴于SPARQL查询的结果,只有可能的顺序是词典编纂,这无法帮助用户区分首先应该查看哪些返回值。从这个意义上说,希望有一个节点“相关性”的概念。一个相关的问题是分析社交网络数据。大多数社交网络分析在很大程度上集中在寻找社交群体和发现个人在社交网络中的重要性。然而,这项工作通常将社交网络视为具有单一类型连接的图形,边缘代表社交通信或友谊的存在。例如,为具有许多不同类型的语义连接的社交网络开发的方法不多。因此,在查询语义丰富的社交网络数据方面的工作很少。
课程简介: As semantic web based applications are gaining popularity, very large RDF documents are becoming common. SPARQL is the de-facto standard in querying RDF data and research on efficient implementations of SPARQL interfaces for very large RDF graphs has attracted a great deal of interest in the recent years. However, in large datasets, the user faces the problem that the result set for her queries can be large. In this situation there is no clear for the user, from where to start looking at the results, since all of them are equally valid. Moreover, given the result of a SPARQL query, the only possible order is lexicographical which doesn’t help the user to distinguish which of the returned values should she look first. In this sense, it would be desirable to have a notion of “relevance” of nodes. A related problem is that of analyzing social network data. Most social network analysis concentrates heavily on finding social groups and finding the importance of individuals in a social network. However, this work generally considers the social network as a graph with a single type of connection, edges representing the existence of social communication or friendship for example. There are not many methods developed for social networks with many different types of semantic connections. As a result, there is very little work on querying of semantically rich social network data.
关 键 词: 语义Web; RDF文档; 数据集
课程来源: 视频讲座网
最后编审: 2019-09-17:lxf
阅读次数: 36