Abstract
Wikipedia, one of the best-known wikis and the world’s largest free online encyclopedia, has embraced the power of collaborative editing to harness collective intelligence. However, using such a wiki to create high-quality articles is not as easy as people imagine, given for instance the difficulty of reusing knowledge already available in Wikipedia. As a result, the heavy burden of upbuilding and maintaining the ever-growing online encyclopedia still rests on a small group of people. In this article, we aim at facilitating wiki authoring by providing annotation recommendations, thus lightening the burden of both contributors and administrators. We leverage the collective wisdom of the users by exploiting Semantic Web technologies with Wikipedia data and adopt a unified algorithm to support link, category, and semantic relation recommendation. A prototype system named EachWiki is proposed and evaluated. The experimental results show that it has achieved considerable improvements in terms of effectiveness, efficiency and usability. The proposed approach can also be applied to other wiki-based collaborative editing systems.
- Adafre, S. F. and de Rijke, M. 2005. Discovering missing links in wikipedia. In Proceedings of the 3rd International Workshop on Link Discovery (LinkKDD’05). ACM, New York, NY, 90--97. Google ScholarDigital Library
- Ankolekar, A., Krötzsch, M., Tran, T., and Vrandecic, D. 2007. The two cultures: Mashing up web 2.0 and the semantic web. In Proceedings of the 16th International Conference on World Wide Web (WWW’07). ACM, New York, NY, 825--834. Google ScholarDigital Library
- Auer, S. and Lehmann, J. 2007. What have innsbruck and leipzig in common? Extracting semantics from wiki content. In Proceedings of the 4th European Conference on the Semantic Web: Research and Applications (ESWC’07). Springer, 503--517. Google ScholarDigital Library
- Baeza-Yates, R. A. and Ribeiro-Neto, B. 1999. Modern Information Retrieval. Addison-Wesley Longman Publishing Co., Inc., Boston, MA. Google ScholarDigital Library
- Fu, L., Wang, H., Zhu, H., Zhang, H., Wang, Y., and Yu, Y. 2007. Making more Wikipedians: Facilitating semantics reuse for Wikipedia authoring. In Proceedings of the 6th International the Semantic Web and 2nd Asian Conference on Asian Semantic Web Conference (ISWC’07/ASWC’07). Springer, 128--141. Google ScholarDigital Library
- Gabrilovich, E. and Markovitch, S. 2007. Computing semantic relatedness using Wikipedia-based explicit semantic analysis. In Proceedings of the 20th International Joint Conference on Artifical Intelligence. Morgan Kaufmann Publishers Inc., San Francisco, CA, 1606--1611. Google ScholarDigital Library
- Giles, J. 2005. Internet encyclopaedias go head to head. Nature 438, 7070, 900--901.Google Scholar
- Goldman, E. 2010. The future of internet content and services: Wikipedia’s labor squeeze and its consequences. J. Telecomm. High Techn. Law 8, 157--613.Google Scholar
- Hepp, M., Bachlechner, D., and Siorpaes, K. 2006. Ontowiki: Community-driven ontology engineering and ontology usage based on wikis. In Proceedings of the International Symposium on Wikis (WikiSym’06). ACM, New York, NY, 143--144. Google ScholarDigital Library
- Liu, T.-Y., Yang, Y., Wan, H., Zeng, H.-J., Chen, Z., and Ma, W.-Y. 2005. Support vector machines classification with a very large-scale taxonomy. SIGKDD Explor. Newslett. 7, 36--43. Google ScholarDigital Library
- Milne, D. 2007. Computing semantic relatedness using wikipedia link structure. In Proceedings of the 5th New Zealand Computer Science Research Student Conference (NZCSRSC’07).Google Scholar
- Oren, E., Gerke, S., and Decker, S. 2007. Simple algorithms for predicate suggestions using similarity and co-occurrence. In Proceedings of the 4th European Conference on the Semantic Web: Research and Applications (ESWC’07). Springer, 160--174. Google ScholarDigital Library
- Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., and Riedl, J. 1994. Grouplens: An open architecture for collaborative filtering of netnews. In Proceedings of the ACM Conference on Computer Supported Cooperative Work (CSCW’94). ACM, New York, NY, 175--186. Google ScholarDigital Library
- Schaffert, S. 2006. Ikewiki: A semantic wiki for collaborative knowledge management. In Proceedings of the 15th IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises. IEEE, 388--396. Google ScholarDigital Library
- Siorpaes, K. and Hepp, M. 2007. Myontology: The marriage of ontology engineering and collective intelligence. In Proceedings of the 4th European Conference on the Semantic Web: Research and Applications (Workshop: Bridging the Gap between SW and 2.0) (ESWC’07). 127--138.Google Scholar
- Strube, M. and Ponzetto, S. P. 2006. Wikirelate! computing semantic relatedness using Wikipedia. In Proceedings of the 21st National Conference on Artificial Intelligence. Vol. 2, AAAI Press, 1419--1424. Google ScholarDigital Library
- Swartz, A. 2006. Raw thought: Making more wikipedians. http://www.aaronsw.com/weblog/morewikipedians.Google Scholar
- Völkel, M., Krötzsch, M., Vrandecic, D., Haller, H., and Studer, R. 2006. Semantic wikipedia. In Proceedings of the 15th International Conference on World Wide Web (WWW’06). ACM, New York, NY, 585--594. Google ScholarDigital Library
- Voss, J. 2006. Collaborative thesaurus tagging the wikipedia way. http://arxiv.org/abs/cs/0604036.Google Scholar
- Wales, J. 2004. Wikipedia sociographics. Talk at the 21st Chaos Communication Congress.Google Scholar
- Zlatić, V., Božičević, M., Štefančić, H., and Domazet, M. 2006. Wikipedias: Collaborative web-based encyclopedias as complex networks. Phys. Rev. E 74, 1, 016115.Google ScholarCross Ref
Index Terms
- EachWiki: Facilitating Wiki Authoring by Annotation Suggestion
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