S Schulz1, L Jansen. 1. Institut für Medizinische Informatik, Statistik und Dokumentation, Medizinische Universität Graz, Auenbruggerplatz 2/V, 8036 Graz, Austria. E-mail: stefan.schulz@medunigraz.at.
Abstract
OBJECTIVES: Medical decision support and other intelligent applications in the life sciences depend on increasing amounts of digital information. Knowledge bases as well as formal ontologies are being used to organize biomedical knowledge and data. However, these two kinds of artefacts are not always clearly distinguished. Whereas the popular RDF(S) standard provides an intuitive triple-based representation, it is semantically weak. Description logics based ontology languages like OWL-DL carry a clear-cut semantics, but they are computationally expensive, and they are often misinterpreted to encode all kinds of statements, including those which are not ontological. METHOD: We distinguish four kinds of statements needed to comprehensively represent domain knowledge: universal statements, terminological statements, statements about particulars and contingent statements. We argue that the task of formal ontologies is solely to represent universal statements, while the non-ontological kinds of statements can nevertheless be connected with ontological representations. To illustrate these four types of representations, we use a running example from parasitology. RESULTS: We finally formulate recommendations for semantically adequate ontologies that can efficiently be used as a stable framework for more context-dependent biomedical knowledge representation and reasoning applications like clinical decision support systems.
OBJECTIVES: Medical decision support and other intelligent applications in the life sciences depend on increasing amounts of digital information. Knowledge bases as well as formal ontologies are being used to organize biomedical knowledge and data. However, these two kinds of artefacts are not always clearly distinguished. Whereas the popular RDF(S) standard provides an intuitive triple-based representation, it is semantically weak. Description logics based ontology languages like OWL-DL carry a clear-cut semantics, but they are computationally expensive, and they are often misinterpreted to encode all kinds of statements, including those which are not ontological. METHOD: We distinguish four kinds of statements needed to comprehensively represent domain knowledge: universal statements, terminological statements, statements about particulars and contingent statements. We argue that the task of formal ontologies is solely to represent universal statements, while the non-ontological kinds of statements can nevertheless be connected with ontological representations. To illustrate these four types of representations, we use a running example from parasitology. RESULTS: We finally formulate recommendations for semantically adequate ontologies that can efficiently be used as a stable framework for more context-dependent biomedical knowledge representation and reasoning applications like clinical decision support systems.
Authors: Bernard Gibaud; Germain Forestier; Carolin Feldmann; Giancarlo Ferrigno; Paulo Gonçalves; Tamás Haidegger; Chantal Julliard; Darko Katić; Hannes Kenngott; Lena Maier-Hein; Keno März; Elena de Momi; Dénes Ákos Nagy; Hirenkumar Nakawala; Juliane Neumann; Thomas Neumuth; Javier Rojas Balderrama; Stefanie Speidel; Martin Wagner; Pierre Jannin Journal: Int J Comput Assist Radiol Surg Date: 2018-07-13 Impact factor: 2.924
Authors: John H Holmes; James Beinlich; Mary R Boland; Kathryn H Bowles; Yong Chen; Tessa S Cook; George Demiris; Michael Draugelis; Laura Fluharty; Peter E Gabriel; Robert Grundmeier; C William Hanson; Daniel S Herman; Blanca E Himes; Rebecca A Hubbard; Charles E Kahn; Dokyoon Kim; Ross Koppel; Qi Long; Nebojsa Mirkovic; Jeffrey S Morris; Danielle L Mowery; Marylyn D Ritchie; Ryan Urbanowicz; Jason H Moore Journal: Methods Inf Med Date: 2021-07-19 Impact factor: 1.800