Ontologies play a major role in life sciences, enabling a number of applications. Obtaining formalized knowledge from unstructured data is especially relevant for biomedical domain, since the amount of textual biomedical data has been growing exponentially. The aim of this paper is to develop a method of creating formal definitions for biomedical concepts using textual information from scientific literature (PubMed abstracts), encyclopedias (Wikipedia), controlled vocabularies (MeSH) and the

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... . The knowledge representation formalism of choice is Description Logic as it allows for integrating the newly acquired axioms in existing biomedical ontologies (e.g. SNOMED) as well as for automated reasoning on top of them. The work is specifically focused on extracting non-taxonomic relations and their instances from natural language texts. It encompasses the analysis, description, implementation and evaluation of the supervised relation extraction pipeline.