Knowledge Acquisition Through Ontologies from Medical Natural Language Texts

OntologiesareusedtorepresentknowledgeandtheyhavebecomeveryimportantintheSemantic Webera.Ontologiesevolvecontinuouslyduringtheirlifecycletoadapttonewrequirementsand needs,especiallyinthebiomedicalfield,wherethenumberofontologiesandtheircomplexityhave increasedduringthelastyears.Ontheotherhand,avastamountofclinicalknowledgeresidesin natural language texts. For these reasons, building and maintaining biomedical ontologies from naturallanguagetextsisarelevantandchallengingissue.Inordertoprovideageneralsolutionand tominimize theexperts’participationduring theontologyenrichingprocess,amethodologyfor extractingtermsandrelationsfromnaturallanguagetextsisproposedinthiswork.Thisframework isbasedonlinguisticandstatisticalmethodsandsemanticrolelabelingtechnologies,havingbeen validatedinthedomainofdiabetes,wheretheyhaveobtainedencouragingresultswithanF-measure of82.1%and79.9%forconceptsandrelations,respectively. KeywORdS Ontology Engineering from the Text, Ontology Evolution, Ontology Learning, Semantic Role Labeling, Term Extraction

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