Instance-Based Learning Algorithms
Machine Learning
Text categorization for multiple users based on semantic features from a machine-readable dictionary
ACM Transactions on Information Systems (TOIS)
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Ranking Function Optimization for Effective Web Search by Genetic Programming: An Empirical Study
HICSS '04 Proceedings of the Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS'04) - Track 4 - Volume 4
Towards the self-annotating web
Proceedings of the 13th international conference on World Wide Web
Gimme' the context: context-driven automatic semantic annotation with C-PANKOW
WWW '05 Proceedings of the 14th international conference on World Wide Web
Discovering missing links in Wikipedia
Proceedings of the 3rd international workshop on Link discovery
Proceedings of the 15th international conference on World Wide Web
ACM SIGIR Forum
Semantic annotation, indexing, and retrieval
Web Semantics: Science, Services and Agents on the World Wide Web
Semantic annotation for knowledge management: Requirements and a survey of the state of the art
Web Semantics: Science, Services and Agents on the World Wide Web
NLDB'05 Proceedings of the 10th international conference on Natural Language Processing and Information Systems
BlueFinder: recommending wikipedia links using DBpedia properties
Proceedings of the 5th Annual ACM Web Science Conference
Hi-index | 0.00 |
Compared with plain-text resources, the ones in "semi-semantic" web sites, such asWikipedia, contain high-level semantic information which will benefit various automatically annotating tasks on themself. In this paper, we propose a "collaborative annotating" approach to automatically recommend categories for a Wikipedia article by reusing category annotations from its most similar articles and ranking these annotations by their confidence. In this approach, four typical semantic features in Wikipedia, namely incoming link, outgoing link, section heading and template item, are investigated and exploited as the representation of articles to feed the similarity calculation. The experiment results have not only proven that these semantic features improve the performance of category annotating, with comparison to the plain text feature; but also demonstrated the strength of our approach in discovering missing annotations and proper level ones for Wikipedia articles.