| Kaifeng Xu|
(Alternative names for this author)
|Co-authors||Haofen Wang, Lei Zhang, Qiaoling Liu, Yiqin Yu, Yue Pan|
|Authorship||Publications (1), datasets (0), tools (0)|
|Citations||Total (0), average (0), median (0), max (0), min (0)|
|DBLP · Google Scholar|
|Export and share|
|BibTeX, CSV, RDF, JSON|
|Browse properties · List of authors|
Kaifeng Xu is an author.
PublicationsOnly those publications related to wikis are shown here.
|Title||Keyword(s)||Published in||Language||DateThis property is a special property in this wiki.||Abstract||R||C|
|Catriple: Extracting triples from wikipedia categories||Lecture Notes in Computer Science||English||2008||As an important step towards bootstrapping the Semantic Web, many efforts have been made to extract triples from Wikipedia because of its wide coverage, good organization and rich knowledge. One kind of important triples is about Wikipedia articles and their non-isa properties, e.g. (Beijing, country, China). Previous work has tried to extract such triples from Wikipedia infoboxes, article text and categories. The infobox-based and text-based extraction methods depend on the infoboxes and suffer from a low article coverage. In contrast, the category-based extraction methods exploit the widespread categories. However, they rely on predefined properties, which is too effort-consuming and explores only very limited knowledge in the categories. This paper automatically extracts properties and triples from the less explored Wikipedia categories so as to achieve a wider article coverage with less manual effort. We manage to realize this goal by utilizing the syntax and semantics brought by super-sub category pairs in Wikipedia. Our prototype implementation outputs about 10M triples with a 12-level confidence ranging from 47.0% to 96.4%, which cover 78.2% of Wikipedia articles. Among them, 1.27M triples have confidence of 96.4%. Applications can on demand use the triples with suitable confidence.||0||0|