An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
OntoKhoj: a semantic web portal for ontology searching, ranking and classification
WIDM '03 Proceedings of the 5th ACM international workshop on Web information and data management
Swoogle: a search and metadata engine for the semantic web
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Ontology ranking based on the analysis of concept structures
Proceedings of the 3rd international conference on Knowledge capture
Similarity of Semantic Relations
Computational Linguistics
OLS: An Ontology Based Information System
CISIS '08 Proceedings of the 2008 International Conference on Complex, Intelligent and Software Intensive Systems
Measuring semantic similarity by latent relational analysis
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Modelling ontology evaluation and validation
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
Ontology selection for the real semantic web: how to cover the queen's birthday dinner?
EKAW'06 Proceedings of the 15th international conference on Managing Knowledge in a World of Networks
Semantic ranking of web pages based on formal concept analysis
Journal of Systems and Software
Ontological map of service oriented architecture for shared services management
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Ontology reuse is recommended as a key factor to develop cost-effective and high-quality ontologies because it could reduce development costs by avoiding rebuilding existing ontologies. Selecting the desired ontology from existing ontologies is essential for ontology reuse. Until now, much research on ontology selection has focused on lexical-level support. However, in these cases, it is almost impossible to find an ontology that includes all the concepts matched by the search terms at the semantic level. Finding an ontology that meets users' needs requires a new ontology selection and ranking mechanism based on semantic similarity matching. We propose an ontology selection and ranking model consisting of selection standards and metrics based on better semantic matching capabilities. The model we propose presents two novel features different from previous research models. First, it enhances the ontology selection and ranking method practically and effectively by enabling semantic matching of taxonomy or relational linkage between concepts. Second, it identifies what measures should be used to rank ontologies in the given context and what weight should be assigned to each selection measure.