Semantic similarity-driven decision support in the skeletal dysplasia domain

Razan, Paul, Groza, Tudor, Zankl, Andreas and Hunter, Jane (2012). Semantic similarity-driven decision support in the skeletal dysplasia domain. In: Philippe Cudré-Mauroux, Jeff Heflin, Evren Sirin, Tania Tudorache, Jérôme Euzenat, Manfred Hauswirth, Josiane Xavier Parreira, Jim Hendler, Guus Schreiber, Abraham Bernstein and Eva Blomqvist, The Semantic Web - ISWC 2012: 11th International SemanticWeb Conference proceedings, part II. 11th International Semantic Web Conference (ISWC 2012), Boston, MA, United States, (164-179). 11-15 November 2012. doi:10.1007/978-3-642-35173-0_11


Author Razan, Paul
Groza, Tudor
Zankl, Andreas
Hunter, Jane
Total Author Count Override 5
Title of paper Semantic similarity-driven decision support in the skeletal dysplasia domain
Conference name 11th International Semantic Web Conference (ISWC 2012)
Conference location Boston, MA, United States
Conference dates 11-15 November 2012
Proceedings title The Semantic Web - ISWC 2012: 11th International SemanticWeb Conference proceedings, part II   Check publisher's open access policy
Journal name Lecture Notes in Computer Science   Check publisher's open access policy
Place of Publication Heidelberg, Germany
Publisher Springer
Publication Year 2012
Sub-type Fully published paper
DOI 10.1007/978-3-642-35173-0_11
ISBN 9783642351723
9783642351730
ISSN 0302-9743
1611-3349
Editor Philippe Cudré-Mauroux
Jeff Heflin
Evren Sirin
Tania Tudorache
Jérôme Euzenat
Manfred Hauswirth
Josiane Xavier Parreira
Jim Hendler
Guus Schreiber
Abraham Bernstein
Eva Blomqvist
Volume 7650
Start page 164
End page 179
Total pages 16
Collection year 2013
Language eng
Abstract/Summary Biomedical ontologies have become a mainstream topic in medical research. They represent important sources of evolved knowledge that may be automatically integrated in decision support methods. Grounding clinical and radiographic findings in concepts defined by a biomedical ontology, e.g., the Human Phenotype Ontology, enables us to compute semantic similarity between them. In this paper, we focus on using such similarity measures to predict disorders on undiagnosed patient cases in the bone dysplasia domain. Different methods for computing the semantic similarity have been implemented. All methods have been evaluated based on their support in achieving a higher prediction accuracy. The outcome of this research enables us to understand the feasibility of developing decision support methods based on ontology-driven semantic similarity in the skeletal dysplasia domain.
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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Created: Mon, 15 Oct 2012, 13:50:08 EST by Dr Tudor Groza on behalf of School of Information Technol and Elec Engineering