Learning Formal Definitions for Snomed CT from Text

Yue MaYue Ma, Felix DistelFelix Distel

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Yue Ma, Felix Distel
Learning Formal Definitions for Snomed CT from Text
Technical Report, Chair of Automata Theory, Institute of Theoretical Computer Science, Technische Universität Dresden, volume 13-03, 2013. LTCS-Report

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KurzfassungAbstract
Snomed CT is a widely used medical ontology which is formally expressed in a fragment of the Description Logic EL++. The underlying logics allow for expressive querying, yet make it costly to maintain and extend the ontology. Existing approaches for ontology generation mostly focus on learning superclass or subclass relations and therefore fail to be used to generate Snomed CT definitions. In this paper, we present an approach for the extraction of Snomed CT definitions from natural language texts, based on the distance relation extraction approach. By benefiting from a relatively large amount of textual data for the medical domain and the rich content of Snomed CT, such an approach comes with the benefit that no manually labelled corpus is required. We also show that the type information for Snomed CT concept is an important feature to be examined for such a system. We test and evaluate the approach using two types of texts. Experimental results show that the proposed approach is promising to assist Snomed CT development.
Bemerkung: Note: See http://lat.inf.tu-dresden.de/research/reports.html.
Forschungsgruppe:Research Group: AutomatentheorieAutomata Theory

@techreport{ MaDi-LTCS-13-03,
  address = {Dresden, Germany},
  author = {Yue {Ma} and Felix {Distel}},
  institution = {Chair of Automata Theory, Institute of Theoretical Computer Science, Technische Universit{\"a}t Dresden},
  note = {See http://lat.inf.tu-dresden.de/research/reports.html.},
  number = {13-03},
  title = {{Learning Formal Definitions for Snomed CT from Text}},
  type = {LTCS-Report},
  year = {2013},
}