Making More Wikipedians: Facilitating Semantics Reuse for Wikipedia Authoring

Abstract

Abstract. Wikipedia, a killer application in Web 2.0, has embraced the power of collaborative editing to harness collective intelligence. It can also serve as an ideal Semantic Web data source due to its abundance, influence, high quality and well-structuring. However, the heavy burden of up-building and maintaining such an enormous and ever-growing online encyclopedic knowledge base still rests on a very small group of people. Many casual users may still feel difficulties in writing high quality Wikipedia articles. In this paper, we use RDF graphs to model the key elements in Wikipedia authoring, and propose an integrated solution to make Wikipedia authoring easier based on RDF graph matching, expecting making more Wikipedians. Our solution facilitates semantics reuse and provides users with: 1) a link suggestion module that suggests and auto-completes internal links between Wikipedia articles for the user; 2) a category suggestion module that helps the user place her articles in correct categories. A prototype system is implemented and experimental results show significant improvements over existing solutions to link and category suggestion tasks. The proposed enhancements can be applied to attract more contributors and relieve the burden of professional editors, thus enhancing the current Wikipedia to make it an even better Semantic Web data source.

Automatic content-based categorization of Wikipedia articles

“…Given that we use |L| binary classifiers for our models, this should be feasible, even for large numbers of categories. It would also be interesting to compare our methods to the work by Fu et al (2007), which concentrates on link-based categorization of Wikipedia articles.…”

“…Wikipedia's article contents and its category hierarchy are widely used to produce semantic resources which improve performance on tasks like text classification and keyword extraction (Banerjee, 2007;Gabrilovich and Markovitch, 2007;Minier et al, 2007;Mihalcea and Csomai, 2007;Wang and Domeniconi, 2008;Medelyan et al, 2008). The reverse -using text classification methods to improve Wikipedia's article-category mappings -has attracted less attention (Fu et al, 2007).…”

Q2Semantic: A Lightweight Keyword Interface to Semantic Search

“…Namely, for each K-Node label, we search the Wikipedia database to see whether it matches the title of any article. If so, several semantic features of the article as introduced in [15] are added as additional labels of the K-Node. These features include the title, the anchor texts that link to the article, and the titles of other articles that redirect to the article.…”