Abstract
Ontologies are becoming increasingly important in the biomedical domain since they enable the re-use and sharing of knowledge in a formal, homogeneous and unambiguous way. In the rapidly growing field of biomedicine, knowledge is usually evolving and therefore an ontology maintenance process is required to keep the ontological knowledge up-to-date. This paper presents our approach for populating a formally defined ontology for the allergen domain exploiting PubMed abstracts on allergens and using natural language processing and machine learning techniques. This approach is composed of two stages: locating initially instances of ontology concepts in the PubMed corpus, and finding at a 2nd stage instances’ properties and relations between instances.
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References
Appelt, D.: Introduction to Information Extraction. AI Communications Journal 2(3), 161–172
Boisen, S., Crystal, M., Schwartz, R., Stone, R., Weischedel, R.: Annotating Resources for Information Extraction. In: proceedings of the 2nd International Conference on Language Resources and Evaluation (LREC-2000), Athens, Greece (2000)
Brewster, C., Ciravegna, F., Wilks, Y.: User-centred ontology learning for knowledge management. In: Andersson, B., Bergholtz, M., Johannesson, P. (eds.) NLDB 2002. LNCS, vol. 2553, pp. 203–207. Springer, Heidelberg (2002)
Brusic, V., Petrovsky, N.: Bioinformatics for Characterisation of Allergens, Allergenicity and Allergic Cross-Reactivity. Trends in Immunology 24, 225–228 (2003)
Petasis, G., Karkaletsis, V., Paliouras, G., Androutsopoulos, I., Spyropoulos, C.D.: Ellogon: A New Text Engineering Platform. In: Proceedings of the 3rd International Conference on Language Resources and Evaluation (LREC-2002), Las Palmas, Spain, pp. 72–78 (May 2002)
Freitag, D., McCallum, A.: Information extraction using hmms and shrinkage. In: Workshop on Machine Learning for Information Extraction(AAAI 1999), pp. 31–36 (1999)
GeneBank, http://www.ncbi.nlm.nih.gov/Genbank/GenbankSearch.html
Huang, M.L., Zhu, X.Y., Hao, Y., Payan, D.G., Qu, K.B., Li, M.: Discovering patterns to extract protein-protein interactions from full texts. Bioinformatics 20(18) (2004)
International Union of Immunological Societies, http://www.allergen.org/List.htm
Message Understanding Conference, http://www.itl.nist.gov/iaui/894.02/related_projects/muc/proceedings/muc_7_toc.html
Protégé’s OWL plug-in, http://protege.stanford.edu/plugins/owl/
Protégé Ontology Editor, http://protege.stanford.edu/
Web Ontology Language, http://www.w3.org/TR/owl-ref
Pustejovsky, J., Castano, J., Zhang, J.: Robust Relational Parsing over Biomedical Literature: Extracting Inhibit Relations. In: the Proceedings of the 7th Pacific Symposium on Biocomputing (PSB 2002), pp. 362–373 (2002)
SwissProt, http://au.expasy.org/sprot/
Temkin, J.M., Gilder, M.R.: Extraction of protein interaction information from unstructured text using a context-free grammar. Bioinformatics 19 (2003)
Valarakos, A., Paliouras, G., Karkaletsis, V., Vouros, G.: Name-Matching Algorithm for Supporting Ontology Enrichment. In: Vouros, G.A., Panayiotopoulos, T. (eds.) SETN 2004. LNCS (LNAI), vol. 3025, pp. 381–389. Springer, Heidelberg (2004)
Valarakos, A., Paliouras, G., Karkaletsis, V., Vouros, G.: Enhancing Ontological Knowledge through Ontology Population and Enrichment. In: Motta, E., Shadbolt, N.R., Stutt, A., Gibbins, N. (eds.) EKAW 2004. LNCS (LNAI), vol. 3257, pp. 144–156. Springer, Heidelberg (2004)
Valarakos, A., Karkaletsis, V., Alexopoulou, D., Papadimitriou, E.: Building an Allergens Domain Ontology and Maintaining it using Machine Learning Techniques, NCSR Technical report, 2005/7 (2005), http://www.iit.demokritos.gr/~alexv/publications/alexvTR2004-7.pdf
Zhou, G., Zhang, J., Su, Z., Shen, D., Tan, C.: Recognizing Names in Biomedical Texts: a Machine Learning Approach (2004)
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Valarakos, A.G., Karkaletsis, V., Alexopoulou, D., Papadimitriou, E., Spyropoulos, C.D. (2005). Populating an Allergens Ontology Using Natural Language Processing and Machine Learning Techniques. In: Miksch, S., Hunter, J., Keravnou, E.T. (eds) Artificial Intelligence in Medicine. AIME 2005. Lecture Notes in Computer Science(), vol 3581. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527770_38
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DOI: https://doi.org/10.1007/11527770_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27831-3
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