Effectiveness of lexico-syntactic pattern matching for ontology enrichment with clinical documents

Methods of Information in Medicine

Volumn 50, Issue 5, 2011, Pages 397-407

  Liu, Kaihong ^a   Chapman, W W ^a,b   Savova, G ^c   Chute, C G ^c   Sioutos, N ^d   Crowley, R S ^a,b  

^a UNIVERSITY OF PITTSBURGH SCHOOL OF MEDICINE   (United States)

^b UNIVERSITY OF PITTSBURGH   (United States)

^c MAYO CLINIC   (United States)

^d LOCKHEED MARTIN   (United States)

Author keywords

Knowledge acquisition; Lexico syntactic pattern; Natural language processing; Ontology enrichment; Ontology learning from text

Indexed keywords

ARTICLE; ARTIFICIAL INTELLIGENCE; COMPARATIVE STUDY; EVALUATION; HUMAN; INSTRUMENTATION; LEARNING; MEDICAL INFORMATICS; NATIONAL HEALTH ORGANIZATION; NATURAL LANGUAGE PROCESSING; NOMENCLATURE; PATHOLOGY; RADIOLOGY; SEMANTICS; UNITED STATES;

ARTIFICIAL INTELLIGENCE; HUMANS; LEARNING; MEDICAL INFORMATICS; NATIONAL CANCER INSTITUTE (U.S.); NATURAL LANGUAGE PROCESSING; PATHOLOGY, SURGICAL; RADIOLOGY; SEMANTICS; TERMINOLOGY AS TOPIC; UNITED STATES;

EID: 83455225762     PISSN: 00261270     EISSN: None     Source Type: Journal    
DOI: 10.3414/ME10-01-0020     Document Type: Article

References (55)

1. Bodenreider O. The Unified Medical Language System (UMLS): integrating biomedical terminology. Nucl Acids Res 2004; 32 (Suppl 1): 267-270.
2. Cowell L, Smith B. Infectious disease ontology. In: Sintchenko V, editor. Infectious Disease Infor-matics. New York City: Springer; 2010. pp 373-395.
3. The Gene Ontology Consortium. The Gene Ontology project. Nucl Acids Res 2008; 36 (Suppl 1): 440-444.
4. HL7: HL7 Reference Information Model. Available from: http://www.hl7.org/implement/standards/ rim.cfm.
5. Achour S, Dojat M, Rieux C, Bierling P, Lepage E. A UMLS-based Knowledge Acquisition Tool for Rulebased Clinical Decision Support System Development. J Am Med Inform Assoc 2001; 8: (4): 351-360.
6. Collier N, Kawazoe A, Jin L, Shigematsu M, Dien D, Barrero RA, et al. A multilingual ontology for in-fectious disease surveillance: rationale, design and challenges. Language Resources and Evaluation 2006; (40): 405-413.
7. Kashyap V, Morales A, Hongsermeier T. On implementing clinical decision support: achieving scalability and maintainability by combining business rules and ontologies. In: Proceedings of the Annual Symposium of American Medical Informatics Association 2006. Washington, DC; 2006. pp 414-418.
8. Haynes B, McKibbon A, Wilczynski N, Walter S, Werre S, for the Hedges T. Optimal search strategies for retrieving scientifically strong studies of treatment from Medline: analytical survey. Brit Med J 2005; 330 (7501): 1179.
9. Sneiderman CA, Demner-Fushman D, Marcelo Fiszman M, Ide NC, Rindflesch TC. Knowledgebased methods to help clinicians find answers in MEDLINE. J Am Med Inform Assoc 2007; 14 (6): 772-780.
10. Meystre S, Haug PJ. Natural language processing to extract medical problems from electronic clinical documents: Performance evaluation. J Biomed Inform 2006; 39 (6): 589-599.
11. Liang T, Lin Y-H. Anaphora Resolution for Biomedical Literature by Exploiting Multiple Resources. In: Dale R, Wong K-F, Su J, Kwong OY, editors. Natural Language Processing-IJCNLP. Berlin/Heidelberg: Springer; 2005. pp 742-753.
12. Pustejovsky J, Rumshisky A, Castano J. Rerendering semantic ontologies: Automatic extensions to UMLS through corpus analytics. Language Resources and Evaluation Workshop on Ontologies and Lexical Knowledge Bases. Las Palmas, Canary Islands, Spain; 2002. pp 60-67.
13. Girju R, Badulescu A, Moldovan D. Learning se-mantic constraints for the automatic discovery of part-whole relations. In: Proceedings of the Human Language Technology Conference. Edmonton, Canada; 2003. pp 80-87.
14. Wagner C. End-users as expert system developers. Journal of End User Computing 2000; 12 (3): 3-13.
15. Wagner C. Breaking the knowledge acquisition bottleneck through conversational knowledge management. Information Resources Management 2006; 19 (1): 70-83.
16. Waterman DA. A guide to expert systems. Addison-Wesley Longman Publishing Co., Inc.; 1985.
17. Druss BG, Marcus SC. Growth and decentralization of the medical literature: implications for evidencebased medicine. J Med Libr Assoc 2005; 93 (4): 499-501.
18. Chun H-W, Tsuruoka Y, Kim J-D, Shiba R, Nagata N, Hishiki T. Extraction of gene-disease relations from Medline using domain dictionaries and machine learning. In: Proceedings of Pacific Symposium on Biocomputing. Maui, HI; 2006. pp 4-15.
19. Collier N, Park H, Ogata N, Tateishi Y, Nobata C, Ohta T, et al. The GENIA project: corpus-based knowledge acquisition and information extraction from genome research papers. In: 9th Conference of the European Chapter of the Association for Computational Linguistics. Bergen, Norway; 1999. pp 271-272.
20. Daraselia N, Yuryev A, Egorov S, Novichkova S, Nikitin A, Mazo I. Extracting human protein interactions from MEDLINE using a full-sentence parser. Bioinformatics 2004; 20 (5): 604-611.
21. Chapman WW, Dowling JN, Wagner MM. Fever detection from free-text clinical records for bio-surveillance. J Biomed Inform 2004; 37 (2): 120-127.
22. South BR, Chapman WW, Delisle S, Shen S, Kalp E, Perl T, et al. Optimizing a Syndromic Surveillance Text Classifier for Influenza-like Illness: Does Document Source Matter? In: Proceedings of the Annual Symposium of American Medical Informatics Association. Washington, DC; 2008. pp 692-696.
23. Cornet R, De Keizer NF, Abu-Hanna A. A framework for characterizing terminological systems. Methods Inf Med 2006; 45: 253-266.
24. de Keizer NF, Abu-Hanna A. Understanding terminological systems II: terminology and typology. Methods Inf Med 2000; 39: 22-29.
25. de Keizer NF, Abu-Hanna A, Zwetsloot-Schonl JHM. Understanding terminological systems I: terminology and typology. Methods Inf Med 2000; 39: 16-21.
26. Buitelaar P, Cimiano P, Magnini B. Ontology learning from text: method, evaluation and applications. Breuker J, Dieng R, Guarino N, Mantaras RLd, Mizoguchi R, Musen M, editors. Amsterdam, Berlin, Oxford, Tokyo, Washington DC: IOS Press; 2005.
27. Gomez-Perez A, Manzano-Macho D. An overview of method and tools for ontology learning from texts. The Knowledge Engineering Review 2005; 19 (3): 187-212.
28. Caraballo S. Automatic construction of a hypernym-labeled noun hierarchy from text. In: Proceedings of the 37th Conference on Computational Linguistics. College Park, MD; 1999. pp 120-126.
29. Cederberg S, Widdows D. Using LSA and noun coordination information to improve the precision and recall of automatic hyponymy extraction. In: Proceedings of the 7th Conference on Natural Language Learning. Edmonton, Canada; 2003. pp 111-118.
30. Downey D, Etzioni O, Soderland S, Weld DS. Learning text patterns for Web information extraction and assessment. In: Proceedings of the American Association for Artificial Intelligence Workshop on Adaptive Text Extraction and Mining. San Jose, CA; 2004. pp 50-55.
31. Hearst MA. Automatic acquisition of hyponyms from large text corpora. In: Proceedings of the 12th Conference on Computational Linguistics. Nantes, France; 1992. pp 539-545.
32. Church KW, Hanks P. Word association norms, mutual information, and lexicography. In: Proceedings of the 27th Annual Meeting of the Association for Computational Linguistics. Vancouver, BC, Canada; 1989. pp 76-83.
33. Grefenstette G. Sextant: exploring unexplored contexts for semantic extraction from syntactic analysis. In: Proceedings of the 30th annual meeting of the Association for Computational Linguistics. Newark, DE; 1992. pp 324-326.
34. Grefenstette G. Explorations in automatic thesau-rus discovery. Boston, MA: Kluwer Academic Publisher; 1994.
35. Kavalec M, Svatek V. A study on automated relation labeling in ontology learning. In: Buitelaar P, Cimiano P, Magnini B, editors. Ontology Learning from Text: Method, Evaluation and Applications. Amsterdam, Berlin, Oxford, Tokyo, Washington DC: IOS Press; 2005. pp 44-58.
36. Nenadâ G, Spasiâ I, Ananiadou S. Automatic discovery of term similarities using pattern mining. In: Proceedings of the 2nd International Workshop on Computational Terminology. Taipei, Taiwan: Association for Computational Linguistics; 2002. pp 1-7.
37. Ryu P-M, Choi K-S. Measuring the specificity of terms for automatic hierarchy construction. In: Proceedings of the European Conference on Artificial Intelligence Workshop on Ontology Learning and Population. Valencia, Spain; 2004.
38. Liu K, Hogan WR, Crowley RS. Natural language processing methods and systems for biomedical ontology learning. J Biomed Inform 2010. In press. (c) Schattauer 2011 Methods Inf Med 5/2011 K. Liu et al.: Lexico-syntactic Pattern Match for Ontology Enrichment 407
39. ODIE toolkit. 2010. Available from: http:// bioontology.org/tools/ODIE.html.
40. Crowley RS, Chavan G, Mitchell K, Liu K, Savova G, Chapman W, et al. ODIE-A workbench for cyclic entity recognition and ontology enrichment. In: Proceedings of the Annual Symp of American Medical Informatics Association. Washington, DC; 2010. Submitted.
41. Mukherjea S, Sahay S. Discovering biomedical relations utilizing the World-Wide Web. In: Proceedings of the Pacific Symposium on Biocomputing. Maui, HI; 2006. pp 164-175.
42. Berland M, Charniak E. Finding parts in very large corpora. In: Proceedings of the 37th Conference on Computational Linguistics. College Park, MD; 1999. pp 57-64.
43. Sundblad H, Automatic acquisition of hyponyms and meronyms from question corpora. In: Proceedings of the 15th European Conference on Artificial Intelligence. Lyon, France; 2002.
44. Fiszman M, Rindflesch TC, Kilicoglu H. Integrating a hypernymic proposition interpreter into a se-mantic processor for biomedical texts. In: Proceedings of the Annual Symposium of the American Medical Informatics Association. Washington, DC; 2003. pp 239-243.
45. Health Insurance Portability and Accountability Act of 1996. Available from: http://aspe.hhs.gov/ admnsimp/pl104191.htm.
46. National Cancer Institute Thesaurus (NCIT) 2010. Available from: http://ncit.nci.nih.gov/.
47. Mejino JLV, Rubin DL, Brinkley JF. FMA-RadLex: an application ontology of radiological anatomy derived from the Foundational Model of Anatomy reference ontology. In: Proceedings of the Annual Symposium of the American Medical Informatics Association. Washington, DC; 2008. p 465.
48. Liu K, Chapman W, Hwa R, Crowley RS. Heuristic Sample Selection to Minimize Reference Standard Training Set for a Part-Of-Speech Tagger. J Am Med Inform Assoc 2007; 14 (5): 641-650.
49. GATE. June 2010. Available from: http://gate.ac. uk/.
50. Chapman WW, Dowling JN, Hripcsak G. Evaluation of training with an annotation schema for manual annotation of clinical conditions from emergency department reports. Int J Med Inform 2008; 77 (2): 107-113.
51. Riloff E. Automatically generating extraction patterns from untagged text. In: Proceedings of the 13th National Conference on Artificial Intelligence. Portland, OR; 1996. pp 1044-1049.
52. Xu R, Morgan A, Das AK, Garber A. Investigation of unsupervised pattern learning techniques for bootstrap construction of a medical treatment lexicon. In: Proceedings of the Workshop on BioNLP. Boulder, Colorado; 2009. pp 63-70.
53. Pantel P, Ravich D, Hovy E. Towards terascale knowledge acquisition. In: Proceedings of THe Conference on Computational Linguistics. Barcelona, Spain; 2004. pp 771-777.
54. Snow R, Jurafsky D, Ng AY, editors. Learning syn-tactic patterns for automatic hypernym discovery. Cambridge, MA: MIT Press; 2005.
55. Embarek M, Ferret O. Learning patterns for building resources about semantic relations in the medical domain. In: Proceedings of the 6th International Confernce on Language Resources and Evaluation. Marrakech, Morocco; 2008. pp 2006-2012.