Journal of the American Society for Information Science and Technology
Outlier detection for high dimensional data
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Text mining: generating hypotheses from MEDLINE
Journal of the American Society for Information Science and Technology
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
Literature mining method RaJoLink for uncovering relations between biomedical concepts
Journal of Biomedical Informatics
OntoGen: semi-automatic ontology editor
Proceedings of the 2007 conference on Human interface: Part II
Performance Analysis of Class Noise Detection Algorithms
Proceedings of the 2010 conference on STAIRS 2010: Proceedings of the Fifth Starting AI Researchers' Symposium
Evaluating outliers for cross-context link discovery
AIME'11 Proceedings of the 13th conference on Artificial intelligence in medicine
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
Bisociative knowledge discovery
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
Towards bisociative knowledge discovery
Bisociative Knowledge Discovery
Bridging concept identification for constructing information networks from text documents
Bisociative Knowledge Discovery
Bisociative Knowledge Discovery
Bisociative literature mining by ensemble heuristics
Bisociative Knowledge Discovery
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In bisociative cross-domain literature mining the goal is to identify interesting terms or concepts which relate different domains. This chapter reveals that a majority of these domain bridging concepts can be found in outlier documents which are not in the mainstream domain literature. We have detected outlier documents by combining three classification-based outlier detection methods and explored the power of these outlier documents in terms of their potential for supporting the bridging concept discovery process. The experimental evaluation was performed on the classical migraine-magnesium and the recently explored autism-calcineurin domain pairs.