Fast discovery of association rules
Advances in knowledge discovery and data mining
Journal of the American Society for Information Science and Technology
LitLinker: capturing connections across the biomedical literature
Proceedings of the 2nd international conference on Knowledge capture
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
Bidirectional inference with the easiest-first strategy for tagging sequence data
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
Learning classifiers from only positive and unlabeled data
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Corpus-based and knowledge-based measures of text semantic similarity
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Improving accuracy for identifying related PubMed queries by an integrated approach
Journal of Biomedical Informatics
Complex event extraction at PubMed scale
Bioinformatics
Annual Review of Information Science and Technology
A Hypergraph-based Method for Discovering Semantically Associated Itemsets
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Similarity-based approach for positive and unlabelled learning
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
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The ever-growing literature in biomedicine makes it virtually impossible for individuals to grasp all the information relevant to their interests. Since even experts' knowledge is limited, important associations among key biomedical concepts may remain unnoticed in the flood of information. Discovering those hidden associations is called hypothesis discovery. This paper reports our approach to this problem taking advantage of a triangular chain of relations extracted from published knowledge. We consider such chains of relations as implicit rules to generate potential hypotheses. The generated hypotheses are then compared with newer knowledge for assessing their validity and, if validated, they are served as positive examples for learning a regression model to rank hypotheses. This framework, called supervised hypothesis discovery, is tested on real-world knowledge from the biomedical literature to demonstrate its effectiveness.