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Computación y Sistemas

Print version ISSN 1405-5546

Comp. y Sist. vol.18 n.3 México Jul./Sep. 2014 

Artículos regulares


Inferring Relations and Annotations in Semantic Network: Application to Radiology


Lionel Ramadier1,2, Manel Zarrouk1, Mathieu Lafourcade1, and Antoine Micheau2


1 LIRMM, Montpellier, France.,,

2 IMAIOS, MIBI, Montpellier, France.


Article received on 10/01/2014.
Accepted on 01/02/2014.



Domain-specific ontologies are invaluable despite many challenges associated with their development. In most cases, domain knowledge bases are built with very limited scope without considering the benefits of plunging domain knowledge to a general ontology. Furthermore, most existing resources lack meta-information about association strength (weights) and annotations (frequency information like frequent, rare, etc. or relevance information like pertinent or irrelevant). In this paper, we present a semantic resource for radiology built over an existing general semantic lexical network (JeuxDeMots). This network combines weight and annotations on typed relations between terms and concepts. Some inference mechanisms are applied to the network to improve its quality and coverage. We extend this mechanism to relation annotation. We describe how annotations are handled and how they improve the network by imposing new constraints especially those founded on medical knowledge.

Keywords: Relation inference, lexical semantic network, relation annotation, radiology.





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