Applications of ontologies and text mining in the biomedical domain

Ontology Theory, Management and Design: Advanced Tools and Models

Volumn , Issue , 2010, Pages 261-283

  Jimeno Yepes, A ^a   Berlanga Llavori, R ^b   Rebholz Schuchmann, D ^a  

^a European Bioinformatic Institute   (United Kingdom)

^b UNIVERSITAT JAUME I   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84899355625     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.4018/978-1-61520-859-3.ch012     Document Type: Chapter

References (82)

1. Agirre, E., Ansa, O., Hovy, E., & Martinez, D. (2000). Enriching very large ontologies using the www. In Proceedings of the Ontology Learning Workshop, ECAI, Berlin, Germany
2. Agirre, E., & Rigau, G. (1996). Word sense disambiguation using conceptual density. In Proceedings of the 16th conference on Computational linguistics, August, (pp. 05-09).
3. Alfonseca, E., & Manandhar, S. (2002). Improving an ontology refinement method with hyponymy patterns. In Language Resources and Evaluation. Las Palmas: LREC.
4. Alfonseca, E., & Manandhar, S. (2002). An unsupervised method for general named entity recognition and automated concept discovery. In Proceedings of the 1st International Conference on General WordNet.
5. Amati, G., & Bruza, P. (1999). A logical approach to query reformulation motivated from belief change. In Workshop on logical and uncertainty models for Information Systems, University College London (UCL), London.
6. Aronson, A. R., & Rindflesch, T. C. (1997). Query expansion using the UMLS Metathesaurus. Amia.
7. Baziz, M., & Boughanem, M. Aussenac., & Gilles, N. (2004). The Use of Ontology for Semantic Representation of Documents. In proceedings of Semantic Web and Information Retrieval Workshop, SIGIR, (pp. 38-45).
8. Beisswanger, E., Poprat, M., & Hahn, U. (2008). Lexical Properties of OBO Ontology Class Names and Synonyms. In 3rd International Symposium on Semantic Mining in Biomedicine.
9. Blaschke, C., & Valencia, A. (2002). Automatic Ontology Construction from the Literature. In Genome Informatics Series, (pp. 201-213).
10. Bodner, R. C., & Song, F. (1996). Knowledgebased approaches to query expansion in information retrieval. In Canadian Conference on AI, (pp. 146-158).
11. Brank, J., Grobelnik, M., & Mladeni, D. (2005). A survey of ontology evaluation techniques. In Proceedings of SIKDD.
12. Buckley, C., & Salton, G. (1995). Optimization of relevance feedback weights. In Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval, (pp. 351-357). New York: ACM Press.
13. Buckley, C., Salton, G., Allan, J., & Singhal, A. (1994). Automatic query expansion using SMART: TREC 3. In Text REtrieval Conference.
14. Bunescu, R., Ge, R., Kate, R., Marcotte, E., Mooney, R., Ramani, A. & Wong, Y. (2004). Comparative experiments on learning information extractors for proteins and their interactions in Comparative Experiments on Learning Information Extractors for Proteins and their Interactions. Journal of Artificial Intelligence in Medicine.
15. Caraballo, S. A. (1999). Automatic construction of a hypernym-labeled noun hierarchy from text. In Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics, (pp. 120-126). Morristown, NJ: Association for Computational Linguistics.
16. Chu, W. W., Liu, Z., & Mao, W. (2002). Textual document indexing and retrieval via knowledge sources and data mining. In Communication of the Institute of Information and Computing Machinery (Vol. 5, p. 2). Taiwan: CIICM.
17. Cowie, J., & Lehnert, W. (1996). Information extraction. Communications of the ACM, 39(1), 80-91. doi:10.1145/234173.234209
18. Dagan, I., Marcus, S., & Markovitch, S. (1993). Contextual word similarity and estimation from sparse data. In Proceedings of the 31st conference on Association for Computational Linguistics, (pp. 164-171). Morristown, NJ: Association for Computational Linguistics
19. Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., & Harshman, R. (1990). Indexing by latent semantic analysis. Journal of the American Society for Information Science American Society for Information Science, 41(6), 391-407. doi:10.1002/(SICI)1097-4571(199009)41:6<391::AIDASI1>3.0.CO;2-9
20. Doms, A., & Schroeder, M. (2005). GoPubMed: exploring PubMed with the gene ontology. Nucleic Acids Research,33.
21. Efthimiadis, E. (1996). Query expansion. In Williams, M. E. (Ed.), Annual Review of Information Systems and Technologies (Vol. 31, pp. 121-187). ARIST.
22. Faatz, A., & Steinmetz, R. (2002). Ontology enrichment with texts from the www. In Semantic Web Mining, WS02.
23. Faure, D., & Nédellec, C. (1998). A corpus-based conceptual clustering method for verb frames and ontology acquisition. In LREC workshop on adapting lexical and corpus resources to sublanguages and applications, (pp. 707-728).
24. Faure, D., & Nédellec, C. (1998). ASIUM: learning subcategorization frames and restrictions of selection. In Y. Kodrato, (ed.), 10th European Conference on Machine Learning (ECML 98), Workshop on Text Mining.
25. Fox, E. A. (1980). Lexical relations: Enhancing effectiveness of information retrieval systems. SIGIR Forum, 15(3), 5-36. doi:10.1145/1095403.1095404
26. Gangemi, A., Pisanelli, D. M., & Steve, G. (1998). Ontology Integration: Experiences with Medical Terminologies. In Formal Ontology in Information Systems: Proceedings of the First International Conference (FOIS'98), June 6-8, Trento, Italy. Amsterdam: IOS Press, Inc.
27. Gauch, S., & Smith, J. B. (1993). An expert system for automatic query reformation. Journal of the American Society for Information Science American Society for Information Science, 44(3), 124-136. doi:10.1002/(SICI)10974571(199304)44:3<124::AID-ASI2>3.0.CO;2C
28. Gaudan, S., Kirsch, H., & Rebholz-Schuhmann, D. (2005). Resolving abbreviations to their senses in Medline. Bioinformatics (Oxford, England), 21(18), 3658-3664. doi:10.1093/bioinformatics/bti586
29. Guarino, N. (1997). Semantic matching: Formal ontological distinctions for information organization, extraction, and integration (pp. 139-170). SCIE.
30. Hahn, U., & Schnattinger, K. (1998). Towards text knowledge engineering. In Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence, (pp. 524-531). Menlo Park, CA: American Association for Artificial Intelligence.
31. Hearst, M. A. (1992). Automatic acquisition of hyponyms from large text corpora. Technical Report S2K-92-09.
32. Hearst, M. A. (1998). Automated discovery of wordnet relations. In WordNet: An Electronic Lexical Database and Some of its Applications. Cambridge, MA: MIT Press.
33. Hearst, M. A., Karger, D. R., & Pedersen, J. O. (1995). Scatter/gather as a tool for the navigation of retrieval results. In Working Notes AAAI Fall Symp. AI Applications in Knowledge Navigation.
34. Hearst, M. A., & Pedersen, J. O. (1996). Reexamining the cluster hypothesis: Scatter/gather on retrieval results (pp. 76-84). SIGIR.
35. Hole, W. T., & Srinivasan, S. (2002). Discovering missed synonymy in a large concept-oriented metathesaurus. AMIA.
36. Jacquemin, C. (2001). Spotting and discovering terms through natural language processing. Cambridge, MA: The MIT Press.
37. Jimeno-Yepes, A., Berlanga-Llavori, R., & Rebholz-Schuhmann, D. (In press). Ontology refinement for improved information retrieval. In Information Processing & Management: Special Issue on Semantic Annotations in Information Retrieval.
38. Jimeno Yepes, A., Jimenez-Ruiz, E., BerlangaLlavori, R., & Rebholz-Schuhmann, D. (2008). Use of shared lexical resources for efficient ontological engineering. In Semantic Web Applications and Tools for Life Sciences.
39. Khan, L. R., & Luo, F. (2002). Ontology construction for information selection. In ICTAI '02: Proceedings of the 14th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'02), (pp. 122). Washington, DC: IEEE Computer Society.
40. Köhler, J., Munn, K., Ruegg, A., Skusa, A., & Smith, B. (2006). Quality control for terms and definitions in ontologies and taxonomies. BMC Bioinformatics, 7, 212. doi:10.1186/1471-21057-212
41. Lavrenko, V., & Croft, W. B. (2001). Relevance based language models. In Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval.
42. Lee, J. B., Kim, J. J., & Park, J. C. (2006). Automatic extension of gene ontology with flexible identification of candidate terms. Bioinformatics (Oxford, England), 22(6), 665-670. doi:10.1093/bioinformatics/btl010
43. Li, H. & Abe, N. (1998). Word clustering and disambiguation based on cooccurrence data. CoRR, cmp-lg/9807004.
44. Lin, D. (1998). Automatic retrieval and clustering of similar words. In Proceedings of the 17th international conference on Computational linguistics, (pp. 768-774). Association for Computational Linguistics.
45. Liu, Z., & Chu, W. W. (2005). Knowledge-based query expansion to support scenario-specific retrieval of medical free text. In SAC '05: Proceedings of the 2005 ACM symposium on Applied computing, (pp. 1076-1083). New York: ACM Press.
46. Maedche, A., & Staab, S. (2001). Ontology learning for the semantic web. IEEE Intelligent Systems, 16(2), 72-79. doi:10.1109/5254.920602
47. Mayfield, J. (2003). Information retrieval on the semantic web: Integrating inference and retrieval. In Proceedings of the SIGIR Workshop on the Semantic Web.
48. Missikoff, M., Velardi, P., & Fabriani, P. (2003). Text mining techniques to automatically enrich a domain ontology. [). Amsterdam: Kluwer Academic Publishers.]. Applied Intelligence, 18, 323-340. doi:10.1023/A:1023254205945
49. Mitra, M., Singhal, A., & Buckley, C. (1998). Improving automatic query expansion. In Research and Development in Information Retrieval, (pp. 206-214).
50. Navigli, R., & Velardi, P. (2002). Automatic adaptation of wordnet to domains. In Proceedings of 3rd International Conference on Language Resources and Evaluation.
51. Nie, J. Y., & Jin, F. (2002). Integrating logical operators in query expansion in vector space model. In Workshop on Mathematical/Formal Methods in Information Retrieval, 25thACM-SIGIR, Tampere, Finland, (Vol. 8).
52. Noy, N. F., & Musen, M. A. (1999). An algorithm for merging and aligning ontologies: automation and tool support. In the Proceedings of the Workshop on Ontology Management at Sixteenth National Conference on Artificial Intelligence (AAAI), Orlando, FL.
53. Patterson, D., Rooney, N., Dobrynin, V. & Galushka, M. (2005). Sophia: A novel approach for textual case-based reasoning. IJCAI, 15-20.
54. Pearson, J. (1998). Terms in Context. Studies in Corpus Linguistics, 1. Philadelphia: John Benjamins.
55. Pezik, P., Jimeno-Yepes, A., Lee, V., & RebholzSchuhmann, D. (2008). Static dictionary features for term polysemy identification. Building and evaluating resources for biomedical text mining. LREC Workshop.
56. Porzel, R., & Malaka, R. (2004). A Task-based Approach for Ontology Evaluation. In ECAI Workshop on Ontology Learning and Population, Valencia, Spain.
57. Pratt, W., Hearst, M. A., & Fagan, L. M. (1999). A knowledge-based approach to organizing retrieved documents (pp. 80-85). AAAI/IAAI.
58. Pratt, W., & Wasserman, H. (2000). QueryCat: Automatic Categorization of MEDLINE Queries. Journal of the American Medical Informatics Association, 7, 655-659.
59. Qiu, Y., & Frei, H. P. (1993). Concept-based query expansion. In Proceedings of SIGIR-93, 16th ACM International Conference on Research and Development in Information Retrieval, (pp. 160-169), Pittsburgh.
60. Rebholz-Schuhmann, D., Kirsch, H., Arregui, M., Gaudan, S., Riethoven, M., & Stoehr, P. (2007). EBIMed - Text crunching to gather facts for proteins from Medline. Bioinformatics (Oxford, England), 23(2), e237-e244. doi:10.1093/bioinformatics/btl302
61. Rebholz-Schuhmann, D., Kirsch, H., & Nenadic, G. (2006). IeXML: towards an annotation framework for biomedical semantic types enabling interoperability of text processing modules. SIG BioLink. ISMB.
62. Riloff, E., & Jones, R. (1999). Learning Dictionaries for Information Extraction by Multi-level strapping, Boot-. In Proceedings of the Sixteenth National Conference on Artificial Intelligence, (pp. 1044-1049). Menlo Park, CA: The AAAI Press/MIT Press.
63. Riloff, E., & Shepherd, J. (1997). A corpus-based approach for building semantic lexicons. In C. Cardie & R. Weischedel, (Eds.), Proceedings of the Second Conference on Empirical Methods in Natural Language Processing, (pp. 117-124). Somerset, NJ: Association for Computational Linguistics.
64. Roark, B., & Charniak, E. (1998). Noun-phrase co-occurence statistics for semiautomatic semantic lexicon construction (pp. 1110-1116). COLING-ACL.
65. Roelleke, T. (2003). A frequency-based and a poisson-based definition of the probability of being informative. In Proceedings of the 26th annual international ACM SIGIR conference on research and development in information retrieval, (pp. 227-234). New York: ACM Press.
66. Rosenfeld, B., Feldman, R., Fresko, M., Schler, J., & Aumann, Y. (2004). Teg: a hybrid approach to information extraction (pp. 589-596). CIKM.
67. Rosse, C., Kumar, A., Mejino, J. L. V., Jr., Cook, D. L., Detwiler, L. T., & Smith, B. (2005). A strategy for improving and integrating biomedical ontologies. In proceedings of AMIA, Washington, DC.
68. Safar, B., Kefi, H., & Reynaud, C. (2004). OntoRefiner, a user query refinement interface usable for Semantic Web Portals. In Applications of Semantic Web technologies to web communities. Workshop ECAI.
69. Schwartz, A. S., & Hearst, M. A. (2003). A simple algorithm for identifying abbreviation definitions in biomedical text. Pacific Symposium on Biocomputing, 451, 62.
70. Smith, B., & Rosse, C. (2004). The role of foundational relations in the alignment of biomedical ontologies. Medinfo, 11(Pt 1), 444-448.
71. Spasic, I., Ananiadou, S., McNaught, J., & Kumar, A. (2005). Text mining and ontologies in biomedicine: Making sense of raw text. Briefings in Bioinformatics, 6(3), 239-251. doi:10.1093/bib/6.3.239
72. Srikant, R., & Agrawal, R. (1997). Mining generalized association rules. Future Generation Computer Systems, 13(2-3), 161-180. doi:10.1016/S0167-739X(97)00019-8
73. Stoilos, G., Stamou, G., & Kollias, S. (2005). A string metric for ontology alignment. In 4th International Semantic Web Conference (ISWC), Galway.
74. Thompson, J. D., Holbrook, S. R., Katoh, K., Koehl, P., Moras, D., Westhof, E., & Poch, O. (2005). Mao: a multiple alignment ontology for nucleic acid and protein sequences. Nucleic Acids Research, 33, 4164. doi:10.1093/nar/gki735
75. Tsujii, J. I., & Ananiadou, S. (2005). Thesaurus or logical ontology, which do we need for mining text? Language Resources and Evaluation, 39(1), 77-90. doi:10.1007/s10579-005-2697-0
76. van Rijsbergen, C. J. (1979). Information retrieval (2nd ed.). London: Butterworths.
77. Voorhees, E. M. (1994). Query expansion using lexical-semantic relations. In Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval, (pp. 61-69). New York: Springer-Verlag, Inc.
78. Widdows, D. (2003). Unsupervised methods for developing taxonomies by combining syntactic and statistical information. HLT-NAACL.
79. Wilbur, W. J., & Kim, W. (2001). Flexible Phrase Based Query Handling Algorithms. In Proceedings of the ASIST Annual Meeting, 38, 438-449.
80. Xu, J., & Croft, W. B. (1996). Query expansion using local and global document analysis. In Proceedings of the Nineteenth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, (pp. 4-11).
81. Yu, A. C. (2006). Methods in biomedical ontology. Journal of Biomedical Informatics, 39(3), 252-266. doi:10.1016/j.jbi.2005.11.006
82. Zhang, S., & Bodenreider, O. (2004). Investigating implicit knowledge in ontologies with application to the anatomical domain. In Pacific Symposium on Biocomputing, (pp. 250-261).