Constructing Biological Knowledge Bases by Extracting Information from Text Sources

Proceedings of the 7th International Conference on Intelligent Systems for Molecular Biology, ISMB 1999

Volumn , Issue , 1999, Pages 77-86

  Craven, Mark ^a   Kumlien, Johan ^a  

^a Carnegie Mellon University ^*  (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); DATA MINING; INFORMATION RETRIEVAL; LEARNING ALGORITHMS; LEARNING SYSTEMS; TEXT PROCESSING;

BIOLOGICAL INFORMATION; EXTRACTING INFORMATION; LABELED TRAINING DATA; LEARNING METHODS; MACHINE LEARNING METHODS; MAPPING INFORMATION; RELATIONAL LEARNING; RESEARCH EFFORTS; TEXT CLASSIFICATION METHODS; TEXT SOURCES;

MOLECULAR BIOLOGY;

ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATION; BAYES THEOREM; COMPUTER PROGRAM; FACTUAL DATABASE; MEDLINE; PUBLICATION; STATISTICAL MODEL;

ARTIFICIAL INTELLIGENCE; AUTOMATION; BAYES THEOREM; DATABASES, FACTUAL; MEDLINE; MODELS, STATISTICAL; PUBLICATIONS; SOFTWARE;

EID: 0033290514     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (34)

1. Andrade, M. A., and Valencia, A. 1997. Automatic annotation for biological sequences by extraction of keywords from MEDLINE abstracts. In Proceedings of the Fifth International Conference on Intelligent Systems for Molecular Biology, 25-32. Halkidiki, Greece: AAAI Press.
2. Boland, M. V.; Markey, M. K.; and Murphy, R. F. 1996. Automated classification of protein localization patterns. Molecular Biology of the Cell 8(346a).
3. Califf, M. E. 1998. Relational Learning Techniques for Natural Language Extinction. Ph.D. Dissertation, Computer Science Department, University of Texas, Austin, TX. AI Technical Report. 98-276. "
4. Cardie, C. 1997. Empirical methods in information extraction. AI Magazine 18(4):65-80.
5. Cestnik, B. 1990. Estimating probabilities: A crucial task in machine learning. In Proceedings of the Ninth European Conference on Artificial Intelligence, 147-150. Stockholm. Sweden: Pitman.
6. Cowie, J., and Lehnert, W. 1996. Information extraction. Communications of the ACM 39(1):80-91.
7. Domingos, P., and Pazzani, M. 1997. On the optimally of the simple Bayesian classifier under zero-one loss. Machine Learning 29:103-130.
8. Freitag, D. 1998. Multistrategy learning for information extraction. In Proceedings of the Fifteenth International Conference on Machine Learning, 161-169. Madison, WI: Morgan Kaufmann.
9. Fukuda, K.; Tsunoda, T.; Tamura, A.; and Takagi, T. 1998. Toward information extraction: Identifying protein names from biological papers. In Pacific Symposium on Biocomputing, 707-718.
10. Genome Annotation Consortium. 1999. The genome channel, http://compbio.ornl.gov/tools/channel/.
11. Hodges, P. E.; Payne, W. E.; and Carrels,J. I. 1998. Yeast protein database (YPD): A database for the complete proteome of saccharomyces cerevisiae. Nucleic Acids Research 26:68-72.
12. Karp, P.; Riley, M.; Paley, S.; and Pellegrini-Toole, A. 1997. EcoCyc: Electronic encyclopedia of E. coli genes and metabolism. Nucleic Acids Research 25(1).
13. Lathrop, R. H.; Steffen, N. R.; Raphael, M. P.; Deeds-Rubin, S.; Pazzani, M. J.; Cimoch, P.: See, D. M.: and Tilles, J. G. 1998. Knowledge-based avoidance of drug-resistant HIV mutants. In Proceedings of the Tenth Conference on Innovative Applications of Artificial Intelligence. Madison. WI: AAAI Press.
14. Leek, T. 1997. Information extraction using hidden markov models. Master's thesis. Department of Computer Science and Engineering, University of California, San Diego, CA.
15. Lewis, D. D., and Ringuette, M. 1994. A comparison of two learning algorithms for text categorization. In Proceedings of the Third Annual Symposium on Document Analysis and Information Retrieval, 81-93.
16. Mitchell, T. M. 1997. Machine Learning. New York: M c G r a w - H i l l.
17. National Center for Biotechnology Information. 1999. Entrez. http://www.ncbi.nlm.nih.gov/Entrez/.
18. National Library of Medicine. 1999a. P n b m e d. h t t p://w w w. n c b i. n l m. n i h. g o v/P u b M e d/.
19. National Library of Medicine. 1999b. Unified medical language system. http://www.nlm.nih.gov/research/umls/uinlsmain.html.
20. Ohta, Y.; Yamamoto. Y.; Okazaki. T.; Uchiyama, I.; and Takagi. T. 1997. Automatic construction of knowledge base from biological papers. In Proceedings of the Fifth International Conference on Intelligent Systems for Molecular Biology, 218-225. Halkidiki. Greece: AAAI Press.
21. Pearl, J. 1988. Probahalistic Reasoning in Intelligent Systtms: Networks of Plausible Inference. San Mateo, CA: Morgan Kaufmann.
22. Porter. M. F. 1980. An algorithm for suffix stripping. Program 14(3): 127-130.
23. Provost. F., and Fawcett, T. 1998. Robust classification systems for imprecise environments. In Proceedings of the Fifteenth National Conference on Artificial Intelligence.. 706-713. Madison, WI: AAAI Press.
24. Quinlan, J. R. 1990. Learning logical definitions from relations. Machine Learning 5:239-2666.
25. Richards. B. L., and Mooney, R. J. 1992. Learning relations by pathfinding. In Proceedings of the Tenth National Conference on Artificial Intelligence. 50-55. San Jose, CA: AAAI/MIT Press.
26. Riloff, E. 1996. An empirical study of automated dictionary construction for information extraction in three domains. Artificial Intelligence 85:101-134.
27. Riloff, E. 1998. The Sundance sentence analyzer. http://www.cs.utah.edu/projects/nlp/.
28. Rost, B. 1996. PHD: Predicting one-dimensional protein structure by profile based neural networks. Methods in Enzymology 266:525-539.
29. Slattery, S., and Craven. M. 1998. Combining statistical and relational methods for learning in hypertext domains. In Proceedings of the Eighth International Conference on Inductive Logic Programming. Springer Verlag.
30. Soderland, S. 1996. Learning Text Analysis Rules for Domain-speific Natural Language Processing. Ph.D. Dissertation, University of Massachusetts. Department of Computer Science Technical Report 96-087.
31. Soderland, S. 1999. Learning information extraction rules for semi-structured and free text.. Machine Learning.
32. Swanson, D. R., and Smalheiser, N. R. 1997. An interactive system for finding complementary literatures: a stimulus to scientific discovery. Artificial Intelliqence 91:183-203.
33. Weeber, M., and Vos. R. 1998. Extracting expert medical knowledge from texts. In Working Notes of the Intelligent Data Analysis in Medicine and Pharmacology Workshop, 23-28.
34. Xu, Y.; Mural, R. J.: Einstein, J. R.; Shah, M. B.; and Uberbacher, E. C. 1996. GRAIL: A multi-agent neural network system for gene identification. Proceedings of the IEEE 84(10):1544-1552.