Thumbnail
Access Restriction
Open

Author Tsatsaronis, George ♦ Petrova, Alina ♦ Kissa, Maria ♦ Ma, Yue ♦ Distel, Felix ♦ Schroeder, Michael
Source CiteSeerX
Content type Text
File Format PDF
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Formal Definition ♦ Biomedical Concept ♦ Snomed Ct ♦ Snomed Ct Concept ♦ Word N-grams ♦ Major Role ♦ Crucial Process ♦ Medical Subject Heading ♦ Populated Role ♦ Unstructured Text ♦ Main Approach ♦ Life Science ♦ Biomedical Knowledge ♦ Novel Methodology ♦ Causative Agent ♦ Finding Site ♦ Suggested Methodology ♦ Snomed Clinical Term ♦ Associated Morphology ♦ Text Definition
Abstract 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. 1
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Institution Technische Universität Dresden