Name
Abstract | ||
|---|---|---|
In order to overcome the limitations of purely deductive approaches to the tasks of classification and retrieval from ontologies, inductive (instance-based) methods have been proposed as efficient and noise-tolerant alternative. In this paper we propose an original method based on non-parametric learning: the Reduced Coulomb Energy (RCE) Network. The method requires a limited training effort but it turns out to be very effective during the classification phase. Casting retrieval as the problem of assessing the class-membership of individuals w.r.t. the query concepts, we propose an extension of a classification algorithm using RCE networks based on an entropic similarity measure for OWL. Experimentally we show that the performance of the resulting inductive classifier is comparable with the one of a standard reasoner and often more efficient than with other inductive approaches. Moreover, we show that new knowledge (not logically derivable) is induced and the likelihood of the answers may be provided. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-02121-3_26 | ESWC |
Keywords | Field | DocType |
reduced coulomb energy network,rce network,classification phase,original method,inductive classifier,individuals w,entropic similarity measure,classification algorithm,inductive approach,deductive approach,reduced coulomb energy,approximate classification | Ontology (information science),Data mining,Semantic reasoner,Similarity measure,Computer science,Description logic,Electric potential energy,Artificial intelligence,Classifier (linguistics),Machine learning | Conference |
Volume | ISSN | Citations |
5554 | 0302-9743 | 9 |
PageRank | References | Authors |
0.48 | 15 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicola Fanizzi | 1 | 1124 | 90.54 |
| Claudia D'Amato | 2 | 733 | 57.03 |
| Floriana Esposito | 3 | 2434 | 277.96 |