Name
Title | ||
|---|---|---|
Recovering uncertain mappings through structural validation and aggregation with the MoTo system |
Abstract | ||
|---|---|---|
We present an automated ontology matching methodology, supported by various machine learning techniques, as implemented in the system MoTo. The methodology is two-tiered. On the first stage it uses a meta-learner to elicit certain mappings from those predicted by single matchers induced by a specific base-learner. Then, uncertain mappings are recovered passing through a validation process, followed by the aggregation of the individual predictions through linguistic quantifiers. Experiments on benchmark ontologies demonstrate the effectiveness of the methodology. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1145/1774088.1774390 | SAC |
Keywords | Field | DocType |
uncertain mapping,certain mapping,moto system,system moto,individual prediction,specific base-learner,single matchers,structural validation,benchmark ontology,validation process,linguistic quantifiers,automated ontology,description logic,ontology matching,machine learning | Ontology (information science),Ontology alignment,Computer science,Description logic,Artificial intelligence,Machine learning | Conference |
Citations | PageRank | References |
1 | 0.34 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Floriana Esposito | 1 | 2434 | 277.96 |
| Nicola Fanizzi | 2 | 1124 | 90.54 |
| Claudia D'Amato | 3 | 733 | 57.03 |