A knowledge-driven approach to biomedical document conceptualization

Artificial Intelligence in Medicine

Volumn 49, Issue 2, 2010, Pages 67-78

  Zheng, Hai Tao ^a   Borchert, Charles ^b   Jiang, Yong ^a  

^a TSINGHUA UNIVERSITY   (China)

^b BK 21 Plus   (South Korea)

Author keywords

Conceptualization; Gene ontology; Knowledge driven approach; Latent semantic analysis; Medical Subject Heading; Ontology

Indexed keywords

CLUSTER DOCUMENTS; CLUSTERING METHODS; CLUSTERING RESULTS; CONCEPT IDENTIFICATION; CONCEPT STRUCTURES; CONCEPTUAL STRUCTURES; DATA SETS; DOCUMENT COLLECTION; DOMAIN KNOWLEDGE; F-MEASURE; FLEXIBLE FRAMEWORK; GENE ONTOLOGY; HIGH PRECISION; INDUCTION ALGORITHMS; KNOWLEDGE BASIS; KNOWLEDGE-DRIVEN APPROACH; LATENT SEMANTIC ANALYSIS; MEDICAL SUBJECT HEADINGS; ONTOLOGY GENERATION; OTHER ALGORITHMS;

DATA PROCESSING; ONTOLOGY; SEMANTICS;

CLUSTERING ALGORITHMS;

ACCURACY; ARTICLE; BIOMEDICAL DOCUMENT CONCEPTUALIZATION; CLASSIFICATION ALGORITHM; CLUSTER ANALYSIS; CONCEPTUAL FRAMEWORK; CONTROLLED STUDY; INTERMETHOD COMPARISON; KNOWLEDGE; KNOWLEDGE BASE; MEDICAL LITERATURE; PRIORITY JOURNAL; SEMANTICS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CLUSTER ANALYSIS; COMPUTER GRAPHICS; DATA MINING; DATABASE MANAGEMENT SYSTEMS; DATABASES, GENETIC; MEDICAL SUBJECT HEADINGS; NATURAL LANGUAGE PROCESSING; PATTERN RECOGNITION, AUTOMATED; SEMANTICS; USER-COMPUTER INTERFACE; VOCABULARY, CONTROLLED;

EID: 77953362587     PISSN: 09333657     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.artmed.2010.02.005     Document Type: Article

References (44)

1. Raychaudhuri S., Chang J.T., Sutphin P.D., Altman R.B. Associating genes with gene ontology codes using a maximum entropy analysis of biomedical literature. Genome Res 2002, 12(1):203-214.
2. Theodosiou T., Angelis L., Vakali A., Thomopoulos G. Gene functional annotation by statistical analysis of biomedical articles. Int J Med Inform 2007, 76(8):601-613.
3. Zheng H-T, Borchert C, Kim H-G. Exploiting gene ontology to conceptualize biomedical document collections. In: Domingue J, Anutariya C (Eds.), ASWC (3rd Asian Semantic Conference), vol. 5367 of Lecture Notes in Computer Science. Springer; 2008, p. 375-89.
4. Srinivasan P. Meshmap: a text mining tool for medline. Proc. AMIA symp. 2001, 642-646.
5. Vanteru B., Shaik J., Yeasin M. Semantically linking and browsing pubmed abstracts with gene ontology. BMC Genom 2008, 9(Suppl. 1):S10.
6. The open biomedical ontologies. [accessed 29.09.09]. http://www.obofoundry.org/.
7. Ashburner M., Ball C.A., Blake J.A., Botstein D., Butler H., Cherry J.M. Gene ontology: tool for the unification of biology. The gene ontology consortium. Nat Genet 2000, 25(1):25-29.
8. PubMed. [accessed 29.09.09]. http://www.ncbi.nlm.nih.gov/sites/entrez/.
9. Izumitani T., Taira H., Kazawa H., Maeda E. Assigning gene ontology categories (go) to yeast genes using text-based supervised learning methods. CSB'04: proceedings of the 2004 IEEE computational systems bioinformatics conference, IEEE computer society 2004, 503-504.
10. Chen S-S., Kim H. Automated linking pubmed documents with go terms using SVM. J Data Sci 2007, 5(2):259-267.
11. Doms A., Schroeder M. Gopubmed: exploring pubmed with the gene ontology. Nucleic Acids Res 2005, 33(Web Server issue):783-786.
12. Delfs R., Doms A., Kozlenkov A., Schroeder M. Gopubmed: ontology-based literature search applied to geneontology and pubmed. German bioinformatics conference 2004, 169-178.
13. Smith T.C., Cleary J.G. Automatically linking medline abstracts to the gene ontology. ISMB 2003 BioLINK text data mining SIG 2003.
14. Medical Subject Headings. [accessed 29.09.09]. http://www.nlm.nih.gov/mesh/.
15. Srinivasan P., Rindflesch T. Exploring text mining from medline. Proc. AMIA symp 2002, 722-726.
16. Uramoto N., Matsuzawa H., Nagano T., Murakami A., Takeuchi H., Takeda K. A text-mining system for knowledge discovery from biomedical documents. IBM Syst J 2004, 43(3):516-533.
17. Kankar P., Adak S., Sarkar A., Murari K., Sharma G. Medmesh summarizer: text mining for gene clusters. Proceedings of the 2nd SIAM international conference on data mining 2002, 548-562.
18. Abasolo J.M., Gómez M. Melisa: an ontology-based agent for information retrieval in medicine. Proceedings of the first international workshop on the semantic web (SemWeb2000) 2000, 73-82.
19. Djebbari A., Karamycheva S., Howe E., Quackenbush J. MeSHer: identifying biological concepts in microarray assays based on pubmed references and mesh terms. Bioinformatics 2005, 21(15):3324-3326.
20. Olsen K.A., Korfhage R.R., Sochats K.M., Spring M.B., Williams J.G. Visualization of a document collection: the VIBE system. Inf Process Manage 1993, 29(1):69-81.
21. Grobelnik M., Mladenic D. Visualization of news articles. Informatica 2004, 28(4):32-35.
22. Fortuna B., Grobelnik M., Mladenić D. Visualization of text document corpus. Informatica 2005, 29:497-502.
23. Zhu W., Chen C. Storylines: visual exploration and analysis in latent semantic spaces. Comput Graphics 2007, 31(3):338-349.
24. Shaw C.D., Kukla J.M., Soboroff I., Ebert D.S., Nicholas C.K., Zwa A. Interactive volumetric information visualization for document corpus management. Int J Digit Libr 1999, 2(2-3):144-156.
25. Forsyth R., Rada R. Adding an edge in machine learning: applications in expert systems and information retrieval 1986, 198-212. Ellis Horwood Ltd.
26. Hotho A., Staab S. Learning concept hierarchies from text corpora using formal concept analysis. J Artif Intell Res 2005, 24:305-339.
27. Sanderson M., Croft B. Deriving concept hierarchies from text. Proceedings of the 22nd annual international ACM SIGIR conference on research and development in information retrieval 1999, 206-213.
28. Stop_Word_List. Technical report [accessed 29.09.09]. http://www.lextek.com/manuals/onix/stopwords2.html.
29. Phan X-H. Crftagger: Crf English pos tagger. [accessed 29.09.09]. http://crftagger.sourceforge.net/.
30. Miller G.A. Wordnet: a lexical database for english. Commun ACM 1995, 38(11):39-41.
31. X.-H. Phan. Crfchunker: Crf English phrase chunker. [accessed 29.09.09]. http://crfchunker.sourceforge.net/.
32. Baeza-Yates R.A., Ribeiro-Neto B. Modern information retrieval 1999, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA.
33. Deerwester S., Dumais S.T., Furnas G.W., Landauer T.K., Harshman R. Indexing by latent semantic analysis. J Am Soc Inf Sci 1990, 41:391-407.
34. McGuinness DL, van Harmelen F. Owl web ontology language overview. [accessed 29.09.09]. http://www.w3.org/TR/owl-features/.
35. Zamir O., Etzioni O. Web document clustering: a feasibility demonstration. SIGIR'98: proceedings of the 21st annual international ACM SIGIR conference on research and development in information retrieval 1998, 46-54.
36. Osinski S., Weiss D. A concept-driven algorithm for clustering search results. IEEE Intell Syst 2005, 20(3):48-54.
37. Schockaert S. Het Clusteren van Zoekresultaten met behulp van Vaagmieren (Clustering of search results using fuzzy ants). Master thesis, University of Ghent; 2004.
38. Lang NC. A tolerance rough set approach to clustering web search results. Master thesis, Warsaw University; 2004.
39. Carrot2. [accessed 29.09.09]. http://project.carrot2.org/.
40. Steinbach M, Karypis G, Kumar V. A comparison of document clustering techniques. In: KDD workshop on text mining; 2000.
41. Pantel P., Lin D. Document clustering with committees. SIGIR'02: proceedings of the 25th annual international ACM SIGIR conference on research and development in information retrieval 2002, 199-206.
42. Weiss D. Descriptive clustering as a method for exploring text collections. Ph.D. thesis, Poznań University of Technology, Poznań, Poland; 2006.
43. Landauer T.K., Foltz P.W., Laham D. An introduction to latent semantic analysis. Discourse Proces 1998, 25:259-284.
44. Java Matrix Package. [accessed 29.09.09]. http://math.nist.gov/javanumerics/jama/.