Conference proceeding
Ontology-Based Scalable and Portable Information Extraction System to Extract Biological Knowledge from Huge Collection of Biomedical Web Documents
Proceedings of the 2004 IEEE/WIC/ACM International Conference on web intelligence, pp 77-83
20 Sep 2004
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
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.
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Details
- Title
- Ontology-Based Scalable and Portable Information Extraction System to Extract Biological Knowledge from Huge Collection of Biomedical Web Documents
- Creators
- Xiaohua HuT LinIl-Yeol SongXia LinIllhoi YooMark LechnerMin Song
- Publication Details
- Proceedings of the 2004 IEEE/WIC/ACM International Conference on web intelligence, pp 77-83
- Conference
- 2004 IEEE/WIC/ACM International Conference on web intelligence
- Series
- WI '04
- Publisher
- IEEE Computer Society
- Number of pages
- 1
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000224473600011
- Other Identifier
- 991019167320104721
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- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Information Systems