Name
Abstract | ||
|---|---|---|
The paper builds on a previous finding of the same authors that concept similarity can be measured on the basis of small sets of characteristic features, selected separately and independently for every concept of two source ontologies. Extending a previously defined parameter-dependent similarity measure, the paper suggests the application of parameter-free correlation coefficients as concept similarity measures and compares their performance with the performance of the parametric similarity measure. An overall procedure for extensional ontology matching based on the suggested similarity criteria is proposed and empirically tested. In addition, the work includes an evaluation of a novel variable selection technique based on Support Vector Machines (SVMs). |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/CISIS.2010.59 | CISIS |
Keywords | Field | DocType |
support vector machines,parameter-dependent similarity measure,variable selection,novel variable selection technique,concept similarity,characteristic feature,overall procedure,suggested similarity criterion,parametric similarity measure,extensional ontology,extensional ontology matching,concept similarity measure,correlation,support vector machine,semantic similarity,ontologies,ontology matching,feature selection,training data,svm | Semantic similarity,Ontology (information science),Data mining,Ontology alignment,Similarity measure,Pattern recognition,Feature selection,Computer science,Support vector machine,Parametric statistics,Correlation,Artificial intelligence | Conference |
Citations | PageRank | References |
4 | 0.42 | 13 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Konstantin Todorov | 1 | 103 | 14.57 |
| Peter Geibel | 2 | 286 | 26.62 |
| Kai-Uwe Kuehnberger | 3 | 4 | 0.42 |