Rank aggregation methods for the Web
Proceedings of the 10th international conference on World Wide Web
Journal of the American Society for Information Science and Technology
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Statistical mechanics of complex networks
Statistical mechanics of complex networks
Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data
Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data
Journal of the American Society for Information Science and Technology
Literature Mining: Towards Better Understanding of Autism
AIME '07 Proceedings of the 11th conference on Artificial Intelligence in Medicine
An Ensemble Approach to Learning to Rank
FSKD '08 Proceedings of the 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02
Literature mining method RaJoLink for uncovering relations between biomedical concepts
Journal of Biomedical Informatics
RaJoLink: A Method for Finding Seeds of Future Discoveries in Nowadays Literature
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
Semi-supervised ensemble ranking
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Artificial Intelligence Review
Outlier Detection in Cross-Context Link Discovery for Creative Literature Mining
The Computer Journal
Towards creative information exploration based on koestler's concept of bisociation
Bisociative Knowledge Discovery
Bisociative knowledge discovery by literature outlier detection
Bisociative Knowledge Discovery
Exploring the power of outliers for cross-domain literature mining
Bisociative Knowledge Discovery
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In literature mining, the identification of bridging concepts that link two diverse domains has been shown to be a promising approach for finding bisociations as distinct, yet unexplored cross-domain connections which could lead to new scientific discoveries. This chapter introduces the system CrossBee (on line Cross-Context Bisociation Explorer) which implements a methodology that supports the search for hidden links connecting two different domains. The methodology is based on an ensemble of specially tailored text mining heuristics which assign the candidate bridging concepts a bisociation score. Using this score, the user of the system can primarily explore only the most promising concepts with high bisociation scores. Besides improved bridging concept identification and ranking, CrossBee also provides various content presentations which further speed up the process of bisociation hypotheses examination. These presentations include side-by-side document inspection, emphasizing of interesting text fragments, and uncovering similar documents. The methodology is evaluated on two problems: the standard migraine-magnesium problem well-known in literature mining, and a more recent autism-calcineurin literature mining problem.