Ontology-Based Scalable and Portable Information Extraction System to Extract Biological Knowledge from Huge Collection of Biomedical Web Documents

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.

[1]  Stephen E. Robertson,et al.  On Term Selection for Query Expansion , 1991, J. Documentation.

[2]  M. Kanehisa,et al.  A Systematic Analysis of Gene Functions by the Metabolic Pathway Database , 1997 .

[3]  Wynne Hsu,et al.  Integrating Classification and Association Rule Mining , 1998, KDD.

[4]  Daniel Berleant,et al.  Mining MEDLINE: Abstracts, Sentences, or Phrases? , 2001, Pacific Symposium on Biocomputing.

[5]  B J Stapley,et al.  Biobibliometrics: information retrieval and visualization from co-occurrences of gene names in Medline abstracts. , 1999, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[6]  Raymond J. Mooney,et al.  Bottom-Up Relational Learning of Pattern Matching Rules for Information Extraction , 2003, J. Mach. Learn. Res..

[7]  Sergey Brin,et al.  Extracting Patterns and Relations from the World Wide Web , 1998, WebDB.

[8]  Joel D. Martin,et al.  Literature mining in molecular biology , 2002 .

[9]  Jung-Hsien Chiang,et al.  MeKE: Discovering the Functions of Gene Products from Biomedical Literature Via Sentence Alignment , 2003, Bioinform..

[10]  Xiaohua Hu,et al.  Discovering Maximal Generalized Decision Rules Through Horizontal and Vertical Data Reduction , 2001, Comput. Intell..

[11]  T. Takagi,et al.  Toward information extraction: identifying protein names from biological papers. , 1998, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[12]  Gary D Bader,et al.  BIND--The Biomolecular Interaction Network Database. , 2001, Nucleic acids research.

[13]  Luis Gravano,et al.  Snowball: extracting relations from large plain-text collections , 2000, DL '00.

[14]  Ioannis Xenarios,et al.  DIP: The Database of Interacting Proteins: 2001 update , 2001, Nucleic Acids Res..

[15]  Ioannis Xenarios,et al.  DIP: the Database of Interacting Proteins , 2000, Nucleic Acids Res..

[16]  Andrzej Skowron,et al.  Proceedings of the 2005 IEEE / WIC / ACM International Conference on Web Intelligence , 2005 .

[17]  Joseph Weizenbaum,et al.  and Machine , 1977 .

[18]  Toshihisa Takagi,et al.  Automated extraction of information on protein-protein interactions from the biological literature , 2001, Bioinform..

[19]  S. Suhai Theoretical and Computational Methods in Genome Research , 2012, Springer US.

[20]  Stephen Soderland,et al.  Learning Information Extraction Rules for Semi-Structured and Free Text , 1999, Machine Learning.

[21]  William W. Cohen Fast Effective Rule Induction , 1995, ICML.

[22]  Ioannis Xenarios,et al.  Mining literature for protein-protein interactions , 2001, Bioinform..

[23]  Ellen Riloff,et al.  Automatically Generating Extraction Patterns from Untagged Text , 1996, AAAI/IAAI, Vol. 2.

[24]  Miguel A. Andrade-Navarro,et al.  Automatic Extraction of Biological Information from Scientific Text: Protein-Protein Interactions , 1999, ISMB.