Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Journal of the American Society for Information Science
Using latent semantic indexing for literature based discovery
Journal of the American Society for Information Science
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
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
TREC: Experiment and Evaluation in Information Retrieval (Digital Libraries and Electronic Publishing)
Journal of the American Society for Information Science and Technology
Using statistical and knowledge-based approaches for literature-based discovery
Journal of Biomedical Informatics
Letter to the Editor: Validating discovery in literature-based discovery
Journal of Biomedical Informatics
Reply: Response to ''Validating discovery in literature-based discovery"
Journal of Biomedical Informatics
Journal of Biomedical Informatics
Mining connections between chemicals, proteins, and diseases extracted from Medline annotations
Journal of Biomedical Informatics
Literature-based discovery: Beyond the ABCs
Journal of the American Society for Information Science and Technology
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While medical researchers formulate new hypotheses to test, they need to identify connections to their work from other parts of the medical literature. However, the current volume of information has become a great barrier for this task. Recently, many literature-based discovery (LBD) systems have been developed to help researchers identify new knowledge that bridges gaps across distinct sections of the medical literature. Each LBD system uses different methods for mining the connections from text and ranking the identified connections, but none of the currently available LBD evaluation approaches can be used to compare the effectiveness of these methods. In this paper, we present an evaluation methodology for LBD systems that allows comparisons across different systems. We demonstrate the abilities of our evaluation methodology by using it to compare the performance of different correlation-mining and ranking approaches used by existing LBD systems. This evaluation methodology should help other researchers compare approaches, make informed algorithm choices, and ultimately help to improve the performance of LBD systems overall.