@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},
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 }
}
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