Using latent semantic indexing for literature based discovery
Journal of the American Society for Information Science
Literature-based discovery by lexical statistics
Journal of the American Society for Information Science
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Information Retrieval: A Health and Biomedical Perspective
Information Retrieval: A Health and Biomedical Perspective
LitLinker: capturing connections across the biomedical literature
Proceedings of the 2nd international conference on Knowledge capture
Constructing an associative concept space for literature-based discovery
Journal of the American Society for Information Science and Technology
Natural Language Engineering
Literature-based Discovery
Mining Multiple Level Non-redundant Association Rules through Two-Fold Pruning of Redundancies
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Supporting creativity: towards associative discovery of new insights
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Correlation modelling of complex data – physics, statistics and heuristics
International Journal of Data Analysis Techniques and Strategies
Selecting the links in bisonets generated from document collections
IDA'10 Proceedings of the 9th international conference on Advances in Intelligent Data Analysis
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Linking biomedical concepts is one of the task of the literature-based discovery and permits to identify interesting and hidden relations between seemingly unconnected concepts or entities. Most of existing approaches rely on the assumption that data and underlying literature are static or considered as unchangeable domains. While scientific literature is instead an intrinsically dynamic domain and can change over time: publications may report studies on the same topic conducted one after another over time. In this work, we investigate the task of analysing biomedical literature under the temporal dimension in order to mine links among concepts over time. This provides us a means to unearth linkages which have not been discovered when observing the literature as static but which may have developed over time, when considering the dynamic nature. We propose a computational solution which, assuming a time interval-based discretisation of the literature, explores the spaces of association rules mined in the intervals and chains these rules on the basis of the concept generalisation and information theory criteria. The application to the Swanson's discoveries shows the possibility of the method to re-discover known connections in biomedical terminology. Experiments and comparisons with alternative techniques highlight the additional peculiarities offered by this work.