Recognizing Descriptive Wikipedia Categories for Historical Figures
This work addresses the challenge of information overload in Wikipedia categorization for applications like information retrieval and entity ranking, but it is incremental as it builds on existing coherence methods.
The paper tackles the problem of identifying the most descriptive Wikipedia categories for historical figures by ranking categories based on their coherence, achieving 88.27% agreement with human judgments.
Wikipedia is a useful knowledge source that benefits many applications in language processing and knowledge representation. An important feature of Wikipedia is that of categories. Wikipedia pages are assigned different categories according to their contents as human-annotated labels which can be used in information retrieval, ad hoc search improvements, entity ranking and tag recommendations. However, important pages are usually assigned too many categories, which makes it difficult to recognize the most important ones that give the best descriptions. In this paper, we propose an approach to recognize the most descriptive Wikipedia categories. We observe that historical figures in a precise category presumably are mutually similar and such categorical coherence could be evaluated via texts or Wikipedia links of corresponding members in the category. We rank descriptive level of Wikipedia categories according to their coherence and our ranking yield an overall agreement of 88.27% compared with human wisdom.