Making More Wikipedians: Facilitating Semantics Reuse for Wikipedia Authoring
|Making More Wikipedians: Facilitating Semantics Reuse for Wikipedia Authoring|
|Author(s)||Linyun Fu, Haofen Wang, Haiping Zhu, Huajie Zhang, Yang Wang, Yong Yu|
|Published in||The Semantic Web|
|Keyword(s)||Unknown (Extra: Distributed computer systems, Graph theory, Knowledge based systems, Semantic Web, Semantics, Solutions, Collaborative Editing, COllective INtelligence, Encyclopedic knowledge, Graph matching, High-quality, Integrated solutions, Key elements, Killer applications, Prototype systems, Semantic Web data, Web 2.0, Wikipedia, Information theory)|
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Making More Wikipedians: Facilitating Semantics Reuse for Wikipedia Authoring is a 2008 publication written in English by Linyun Fu, Haofen Wang, Haiping Zhu, Huajie Zhang, Yang Wang, Yong Yu and published in The Semantic Web.
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.
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