On term selection for query expansion
Journal of Documentation
Learning Information Extraction Rules for Semi-Structured and Free Text
Machine Learning - Special issue on natural language learning
Snowball: extracting relations from large plain-text collections
DL '00 Proceedings of the fifth ACM conference on Digital libraries
ELIZA—a computer program for the study of natural language communication between man and machine
Communications of the ACM
Automatic Extraction of Biological Information from Scientific Text: Protein-Protein Interactions
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
Extracting Patterns and Relations from the World Wide Web
WebDB '98 Selected papers from the International Workshop on The World Wide Web and Databases
Bottom-up relational learning of pattern matching rules for information extraction
The Journal of Machine Learning Research
Automatically generating extraction patterns from untagged text
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Keyphrase extraction-based query expansion in digital libraries
Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries
Data Mining and Predictive Modeling of Biomolecular Network from Biomedical Literature Databases
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Mining and analysing scale-free protein protein interaction network
International Journal of Bioinformatics Research and Applications
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Automated discovery and extraction of biological knowledge from biomedical web documents has become essential because of the enormous amount of biomedical literature published each year. In this paper we present an ontology-based scalable and portable information extraction system to automatically extract biological knowledge from huge collection of online biomedical web documents. Our method integrates ontology-based semantic tagging, information extraction and data mining together, automatically learns the patterns based on a few user seed tuples, and then extract new tuples from the biomedical web documents based on the discovered patterns. A novel system SPIE (Scalable and Portable Information Extraction) is implemented and tested on the PuBMed to find the chromatin protein-protein interaction and the experimental results indicate our approach is very effective in extracting biological knowledge from huge collection of biomedical web documents.