Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.13053/CyS-18-3-2024 | COMPUTACION Y SISTEMAS |
Keywords | Field | DocType |
Relation inference, lexical semantic network, relation annotation, radiology | Ontology (information science),Ontology,Annotation,Information retrieval,Domain knowledge,Computer science,Inference,Semantic network,Radiology | Journal |
Volume | Issue | ISSN |
18 | 3 | 1405-5546 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lionel Ramadier | 1 | 1 | 2.42 |
| Manel Zarrouk | 2 | 20 | 9.31 |
| Mathieu Lafourcade | 3 | 120 | 22.29 |
| Antoine Micheau | 4 | 1 | 0.73 |