Populating an allergens ontology using natural language processing and machine learning techniques

Authors:
Alexandros G. Valarakos;Vangelis Karkaletsis;Dimitra Alexopoulou;Elsa Papadimitriou;Constantine D. Spyropoulos
Affiliations:
Software & Knowledge Engineering Lab., Inst. of Informatics and Telecomm, National Center for Scientific Research “Demokritos”, Ag. Paraskevi, Greece;Software & Knowledge Engineering Lab., Inst. of Informatics and Telecomm, National Center for Scientific Research “Demokritos”, Ag. Paraskevi, Greece;Software & Knowledge Engineering Lab., Inst. of Informatics and Telecomm, National Center for Scientific Research “Demokritos”, Ag. Paraskevi, Greece;Software & Knowledge Engineering Lab., Inst. of Informatics and Telecomm, National Center for Scientific Research “Demokritos”, Ag. Paraskevi, Greece;Software & Knowledge Engineering Lab., Inst. of Informatics and Telecomm, National Center for Scientific Research “Demokritos”, Ag. Paraskevi, Greece
Venue:
AIME'05 Proceedings of the 10th conference on Artificial Intelligence in Medicine
Year:
2005

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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.