Difference between revisions of "Method and Tool Support for Classifying Software Languages with Wikipedia"
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| − | '''Method and Tool Support for Classifying Software Languages with Wikipedia''' - scientific work related to Wikipedia quality published in 2013, written by Ralf Lämmel, Dominik Mosen and Andrei Varanovich. | + | '''Method and Tool Support for Classifying Software Languages with Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Ralf Lämmel]], [[Dominik Mosen]] and [[Andrei Varanovich]]. |
== Overview == | == Overview == | ||
| − | Wikipedia provides useful input for efforts on mining taxonomies or ontologies in specific domains. In particular, | + | Wikipedia provides useful input for efforts on mining taxonomies or ontologies in specific domains. In particular, [[Wikipedia]]’s [[categories]] serve classification. In this paper, authors describe a method and a corresponding tool, WikiTax, for exploring Wikipedia’s category graph with the objective of supporting the development of a classification of software languages. The category graph is extracted level by level. The extracted graph is visualized in a tree-like manner. Category attributes (i.e., metrics) such as depth are visualized. Irrelevant edges and nodes may be excluded. These exclusions are documented while using a manageable and well-defined set of ‘exclusion types’ as comments. |
Revision as of 23:20, 29 August 2019
Method and Tool Support for Classifying Software Languages with Wikipedia - scientific work related to Wikipedia quality published in 2013, written by Ralf Lämmel, Dominik Mosen and Andrei Varanovich.
Overview
Wikipedia provides useful input for efforts on mining taxonomies or ontologies in specific domains. In particular, Wikipedia’s categories serve classification. In this paper, authors describe a method and a corresponding tool, WikiTax, for exploring Wikipedia’s category graph with the objective of supporting the development of a classification of software languages. The category graph is extracted level by level. The extracted graph is visualized in a tree-like manner. Category attributes (i.e., metrics) such as depth are visualized. Irrelevant edges and nodes may be excluded. These exclusions are documented while using a manageable and well-defined set of ‘exclusion types’ as comments.
