Difference between revisions of "Topic Calculation and Clustering: an Application to Wikipedia"
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| − | '''Topic Calculation and Clustering: an Application to Wikipedia''' - scientific work related to Wikipedia quality published in 2008, written by Sara E. Garza, Ramón F. Brena and Eduardo H. Ramírez. | + | '''Topic Calculation and Clustering: an Application to Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[Sara E. Garza]], [[Ramón F. Brena]] and [[Eduardo H. Ramírez]]. |
== Overview == | == Overview == | ||
| − | Wikipedia is nowadays one of the most valuable information resources; nevertheless, its current structure, which has no formal organization, does not allow to always have a useful browsing among topics. Moreover, even though most Wikipedia pages include a "See Also" section for navigating through those articles' related Wikipedia pages, the only references included here are those which authors are aware of, leading to | + | Wikipedia is nowadays one of the most valuable information resources; nevertheless, its current structure, which has no formal organization, does not allow to always have a useful browsing among topics. Moreover, even though most [[Wikipedia]] pages include a "See Also" section for navigating through those articles' related Wikipedia pages, the only references included here are those which authors are aware of, leading to in[[completeness]] and other irregularities. In this work, a method for finding related Wikipedia articles is proposed; this method relies on a framework that clusters documents into semantically-calculated topics and selects the closest documents which could enrich the "See Also" section. |
Revision as of 07:30, 16 July 2019
Topic Calculation and Clustering: an Application to Wikipedia - scientific work related to Wikipedia quality published in 2008, written by Sara E. Garza, Ramón F. Brena and Eduardo H. Ramírez.
Overview
Wikipedia is nowadays one of the most valuable information resources; nevertheless, its current structure, which has no formal organization, does not allow to always have a useful browsing among topics. Moreover, even though most Wikipedia pages include a "See Also" section for navigating through those articles' related Wikipedia pages, the only references included here are those which authors are aware of, leading to incompleteness and other irregularities. In this work, a method for finding related Wikipedia articles is proposed; this method relies on a framework that clusters documents into semantically-calculated topics and selects the closest documents which could enrich the "See Also" section.
