| Graph analysis|
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Graph analysis is included as keyword or extra keyword in 0 datasets, 0 tools and 4 publications.
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|Title||Author(s)||Published in||Language||DateThis property is a special property in this wiki.||Abstract||R||C|
|An approach for deriving semantically related category hierarchies from Wikipedia category graphs||Hejazy K.A.
|Advances in Intelligent Systems and Computing||English||2013||Wikipedia is the largest online encyclopedia known to date. Its rich content and semi-structured nature has made it into a very valuable research tool used for classification, information extraction, and semantic annotation, among others. Many applications can benefit from the presence of a topic hierarchy in Wikipedia. However, what Wikipedia currently offers is a category graph built through hierarchical category links the semantics of which are un-defined. Because of this lack of semantics, a sub-category in Wikipedia does not necessarily comply with the concept of a sub-category in a hierarchy. Instead, all it signifies is that there is some sort of relationship between the parent category and its sub-category. As a result, traversing the category links of any given category can often result in surprising results. For example, following the category of "Computing" down its sub-category links, the totally unrelated category of "Theology" appears. In this paper, we introduce a novel algorithm that through measuring the semantic relatedness between any given Wikipedia category and nodes in its sub-graph is capable of extracting a category hierarchy containing only nodes that are relevant to the parent category. The algorithm has been evaluated by comparing its output with a gold standard data set. The experimental setup and results are presented.||0||0|
|Using social network analysis and hierarchical clustering to identify groups in Wikis||Barth F.J.||Proceedings of the SBSC 2010 - 7th Brazilian Symposium on Collaborative Systems||Portuguese||2010||This article explores hierarchical clustering and graph analysis for detection groups in Wikis. Both approaches are explored in this work using historical information about Wiki pages. The results shows that this type of analysis can be used to identify people and groups with similar interests and abilities.||0||0|
|Analysis of community structure in Wikipedia (poster)||Dmitry Lizorkin
|WWW'09 - Proceedings of the 18th International World Wide Web Conference||English||2009||We present the results of a community detection analysis of the Wikipedia graph. Distinct communities in Wikipedia contain semantically closely related articles. The central topic of a community can be identified using PageRank. Extracted communities can be organized hierarchically similar to manually created Wikipedia category structure. Copyright is held by the author/owner(s).||0||0|
|Extracting key terms from noisy and multi-theme documents||Maria Grineva
|WWW'09 - Proceedings of the 18th International World Wide Web Conference||English||2009||We present a novel method for key term extraction from text documents. In our method, document is modeled as a graph of semantic relationships between terms of that document. We exploit the following remarkable feature of the graph: the terms related to the main topics of the document tend to bunch up into densely interconnected sub-graphs or communities, while non-important terms fall into weakly interconnected communities, or even become isolated vertices. We apply graph community detection techniques to partition the graph into thematically cohesive groups of terms. We introduce a criterion function to select groups that contain key terms discarding groups with unimportant terms. To weight terms and determine semantic relatedness between them we exploit information extracted from Wikipedia. Using such an approach gives us the following two advantages. First, it allows effectively processing multi-theme documents. Second, it is good at filtering out noise information in the document, such as, for example, navigational bars or headers in web pages. Evaluations of the method show that it outperforms existing methods producing key terms with higher precision and recall. Additional experiments on web pages prove that our method appears to be substantially more effective on noisy and multi-theme documents than existing methods. Copyright is held by the International World Wide Web Conference Committee (IW3C2).||0||0|