An approach for deriving semantically related category hierarchies from Wikipedia category graphs
|An approach for deriving semantically related category hierarchies from Wikipedia category graphs|
|Author(s)||Hejazy K.A., El-Beltagy S.R.|
|Published in||Advances in Intelligent Systems and Computing|
|Keyword(s)||Category hierarchy, Graph analysis, Hierarchy extraction, Semantic relatedness, Semantic similarity, Wikipedia (Extra: Category hierarchy, Graph analysis, Semantic relatedness, Semantic similarity, Wikipedia, Algorithms, Information systems, Natural language processing systems, Semantics, Websites)|
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An approach for deriving semantically related category hierarchies from Wikipedia category graphs is a 2013 conference paper written in English by Hejazy K.A., El-Beltagy S.R. and published in Advances in Intelligent Systems and Computing.
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.
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