MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
Improving Temporal Language Models for Determining Time of Non-timestamped Documents
ECDL '08 Proceedings of the 12th European conference on Research and Advanced Technology for Digital Libraries
Bioinformatics
Journal of Biomedical Informatics
TRUMIT: a tool to support large-scale mining of text association rules
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Mining association rules in temporal document collections
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
DOG4DAG: semi-automated ontology generation in OBO-Edit and Protégé
Proceedings of the 4th International Workshop on Semantic Web Applications and Tools for the Life Sciences
Hi-index | 0.00 |
Ontologies such as the Medical Subject Headings (MeSH) and the Gene Ontology (GO) play a major role in biology and medicine since they facilitate data integration and the consistent exchange of information between different entities. They can also be used to index and annotate data and literature, thus enabling efficient search and analysis. Unfortunately, maintaining the ontologies manually is a complex, error-prone, and time and personnel-consuming effort. One major problem is the continuous growth of the biomedical literature, which expands by almost 1 million new scientific papers per year, indexed by Medline. The enormous annual increase of scientific publications constitutes the task of monitoring and following the changes and trends in the biomedical domain extremely difficult. For this purpose, approaches that try to learn and maintain ontologies automatically from text and data have been developed in the past. The goal of this paper is to develop temporal classifiers in order to create, for the first time to the best of our knowledge, an automated method that may predict which regions of the MeSH ontology will expand in the near future.