title: Building an allergens ontology and maintaining it using machine learning techniques.
abstract: Ontologies are widely used for formalizing and organizing the knowledge of a particular domain of interest. This facilitates knowledge sharing and re-use by both people and systems. Ontologies are becoming increasingly important in the biomedical domain since they enable knowledge sharing in a formal, homogeneous and unambiguous way. Knowledge in a rapidly growing field such as biomedicine is usually evolving and therefore an ontology maintenance process is required to keep ontological knowledge up-to-date. This work presents our methodology for building a formally defined ontology, maintaining it exploiting machine learning techniques and domain specific corpora, and evaluating it using a well-defined experimental setting. The application of this methodology in the allergen domain is then discussed in detail presenting the ontology built, the specific techniques used and the evaluation settings.
authors.affiliation: Software and Knowledge Engineering Laboratory, Institute of Informatics and Telecommunications, National Centre for Scientific Research (NCSR) "Demokritos", 153 10 Ag. Paraskevi, Athens, Greece. alezv@iit.demokritos.gr
authors.forename: Alexandros G
authors.initials: AG
authors.lastname: Valarakos
authors.forename: Vangelis
authors.initials: V
authors.lastname: Karkaletsis
authors.forename: Dimitra
authors.initials: D
authors.lastname: Alexopoulou
authors.forename: Elsa
authors.initials: E
authors.lastname: Papadimitriou
authors.forename: Constantine D
authors.initials: CD
authors.lastname: Spyropoulos
authors.forename: George
authors.initials: G
authors.lastname: Vouros
mesh_terms: D000485:Allergens / Q000145:classification*; D001185:Artificial Intelligence; D019992:Databases as Topic; D006801:Humans; D019451:Information Management; D051188:Knowledge Bases; D039781:PubMed; D012984:Software; D009626:Terminology as Topic
chemical_list: D000485:Allergens