Learning formal definitions for biomedical concepts

Publikation: Beitrag in FachzeitschriftKonferenzartikelBeigetragenBegutachtung

Abstract

Ontologies such as the SNOMED Clinical Terms (SNOMED CT), and the Medical Subject Headings (MeSH) play a major role in life sciences. Modeling formally the concepts and the roles in this domain is a crucial process to allow for the integration of biomedical knowledge across applications. In this direction we propose a novel methodology to learn formal definitions for biomedical concepts from unstructured text. We evaluate experimentally the suggested methodology in learning formal definitions of SNOMED CT concepts, using their text definitions from MeSH. The evaluation is focused on the learning of three roles which are among the most populated roles in SNOMED CT: Associated Morphology, Finding Site and Causative Agent. Results show that our methodology may provide an Accuracy of up to 75%. For the representation of the instances three main approaches are suggested, namely, Bag of Words, word n-grams and character n-grams.

Details

OriginalspracheEnglisch
FachzeitschriftCEUR Workshop Proceedings
Jahrgang1080
PublikationsstatusVeröffentlicht - 2013
Peer-Review-StatusJa

Konferenz

Titel10th International Workshop on OWL: Experiences and Directions, OWLED 2013 - Co-located with 10th Extended Semantic Web Conference, ESWC 2013
Dauer26 - 27 Mai 2013
StadtMontpellier
LandFrankreich

Externe IDs

ORCID /0000-0003-2848-6949/work/141543342
ORCID /0000-0002-4049-221X/work/142247965

Schlagworte

ASJC Scopus Sachgebiete