Difference between revisions of "Exploring Wikipedia's Category Graph for Query Classification"
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| − | '''Exploring Wikipedia's Category Graph for Query Classification''' - scientific work related to Wikipedia quality published in 2011, written by Milad Alemzadeh, Richard Khoury and Fakhri Karray. | + | '''Exploring Wikipedia's Category Graph for Query Classification''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Milad Alemzadeh]], [[Richard Khoury]] and [[Fakhri Karray]]. |
== Overview == | == Overview == | ||
| − | Wikipedia's category graph is a network of 400,000 interconnected category labels, and can be a powerful resource for many classification tasks. However, its size and the lack of order can make it difficult to navigate. In this paper, authors present a new algorithm to efficiently explore this graph and discover accurate classification labels. Authors implement algorithm as the core of a query classification system and demonstrate its reliability using the KDD CUP 2005 competition as a benchmark. | + | Wikipedia's category graph is a network of 400,000 interconnected category labels, and can be a powerful resource for many classification tasks. However, its size and the lack of order can make it difficult to navigate. In this paper, authors present a new algorithm to efficiently explore this graph and discover accurate classification labels. Authors implement algorithm as the core of a query classification system and demonstrate its [[reliability]] using the KDD CUP 2005 competition as a benchmark. |
Revision as of 20:32, 6 June 2019
Exploring Wikipedia's Category Graph for Query Classification - scientific work related to Wikipedia quality published in 2011, written by Milad Alemzadeh, Richard Khoury and Fakhri Karray.
Overview
Wikipedia's category graph is a network of 400,000 interconnected category labels, and can be a powerful resource for many classification tasks. However, its size and the lack of order can make it difficult to navigate. In this paper, authors present a new algorithm to efficiently explore this graph and discover accurate classification labels. Authors implement algorithm as the core of a query classification system and demonstrate its reliability using the KDD CUP 2005 competition as a benchmark.
