Difference between revisions of "Enriching Wikipedia Vandalism Taxonomy via Subclass Discovery"
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| − | '''Enriching Wikipedia Vandalism Taxonomy via Subclass Discovery''' - scientific work related to Wikipedia quality published in 2011, written by Si-Chi Chin and W. Nick Street. | + | '''Enriching Wikipedia Vandalism Taxonomy via Subclass Discovery''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Si-Chi Chin]] and [[W. Nick Street]]. |
== Overview == | == Overview == | ||
| − | This paper adopts an unsupervised subclass discovery approach to automatically improve the taxonomy of Wikipedia vandalism. Wikipedia vandalism, defined as malicious editing intended to compromise the integrity of the content of articles, exhibits heterogeneous characteristics, making it hard to detect automatically. The categorization of vandalism provides insights on the detection of vandalism instances. Experimental results demonstrate the potential of using supervised and unsupervised learning to reproduce the manual annotation and enrich the predefined knowledge representation. | + | This paper adopts an unsupervised subclass discovery approach to automatically improve the taxonomy of [[Wikipedia]] vandalism. Wikipedia vandalism, defined as malicious editing intended to compromise the integrity of the content of articles, exhibits heterogeneous characteristics, making it hard to detect automatically. The categorization of vandalism provides insights on the detection of vandalism instances. Experimental results demonstrate the potential of using supervised and unsupervised learning to reproduce the manual annotation and enrich the predefined knowledge representation. |
Revision as of 09:51, 1 August 2019
Enriching Wikipedia Vandalism Taxonomy via Subclass Discovery - scientific work related to Wikipedia quality published in 2011, written by Si-Chi Chin and W. Nick Street.
Overview
This paper adopts an unsupervised subclass discovery approach to automatically improve the taxonomy of Wikipedia vandalism. Wikipedia vandalism, defined as malicious editing intended to compromise the integrity of the content of articles, exhibits heterogeneous characteristics, making it hard to detect automatically. The categorization of vandalism provides insights on the detection of vandalism instances. Experimental results demonstrate the potential of using supervised and unsupervised learning to reproduce the manual annotation and enrich the predefined knowledge representation.
