Name
Abstract | ||
|---|---|---|
Recently, several ontologies have been proposed for real life domains, where these propositions are large and voluminous due to the complexity of the domain. Consequently, Ontology Aligning has been attracting a great deal of interest in order to establish interoperability between heterogeneous applications. Although, this research has been addressed, most of existing approaches do not well capture suitable correspondences when the size and structure vary vastly across ontologies. Addressing this issue, we propose in this paper a fuzzy clustering based alignment approach which consists on improving the ontological structure organization. The basic idea is to perform the fuzzy clustering technique over the ontologyâs concepts in order to create clusters of similar concepts with estimation of medoids and membership degrees. The uncertainty is due to the fact that a concept has multiple attributes so to be assigned to different classes simultaneously. Then, the ontologies are aligned based on the generated fuzzy clusters with the use of different similarity techniques to discover correspondences between conceptual entities. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.5220/0005916805940599 | ICEIS |
Keywords | Field | DocType |
Fuzzy C-Medoid, Ontology Aligning, Semantic Similarity, Similarity Measures | Ontology (information science),Semantic similarity,Data mining,Cluster (physics),Fuzzy clustering,Ontology alignment,Ontology,Computer science,Interoperability,Artificial intelligence,Machine learning,Medoid | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rihab Idoudi | 1 | 1 | 0.77 |
| Karim Saheb Ettabaâ | 2 | 2 | 1.59 |
| Kamel Hamrouni | 3 | 41 | 21.73 |
| Basel Solaiman | 4 | 127 | 35.05 |