Affiliations: College of Information Science and Technology, Drexel
University, Philadelphia, PA 19104, USA | Department of Computer Science, San Jose State
University, San Jose, CA 94403, USA
Abstract: Many biological results are published only in plain – text
documents and these documents or their abstracts are collected in web-based
digital libraries such as PubMed and BioMed Central. To expedite the progress
of functional bioinformatics, it is important to efficiently process large
amounts of these documents, to extract these results into a structured format,
and to store them in a database so that these results can be retrieved and
analyzed by biologists and medical researchers. Automated discovery and
extraction of the 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 a semi-supervised efficient learning
approach to automatically extract biological knowledge from the web-based
digital libraries. Our method integrates ontology-based semantic tagging as
well as information extraction and data mining together. Our method
automatically learns the patterns based on a few user seed tuples and then
extracts 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. The experimental results indicate our approach is very
effective in extracting biological knowledge from a huge collection of
biomedical web documents.
Keywords: Information extraction, ontology, semi-supervised learning, UMLS
