@article{keyhere,
	title = {Populating an Allergens Ontology Using Natural Language Processing and Machine Learning Techniques},
	author = {Alexandros G. Valarakos and Vangelis Karkaletsis and Dimitra Alexopoulou and Elsa Papadimitriou and Constantine D. Spyropoulos},
	journal = {Artificial Intelligence in Medicine},
	pages = {256--265},
	url = {http://dx.doi.org/10.1007/11527770_38},
	year = {2005},
	biburl = {http://www.bibsonomy.org/bibtex/2a58567e8063e93ce1da75dcf9615c88d/renew},
	description = {SpringerLink - Book Chapter},
	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.
ER  -},
	keywords = {bioinformatics ontology population }
}