Name
Abstract | ||
|---|---|---|
Many applicative domains require complex multi-relational representations. We propose a family of kernels for relational representations to produce statistical classifiers that can be effectively employed in a variety of such tasks. The kernel functions are defined over the set of objects in a knowledge base parameterized on a notion of context, represented by a committee of concepts expressed through logic clauses. A preliminary feature construction phase based on genetic programming allows for the selection of optimized contexts. An experimental session on the task of similarity search proves the practical effectiveness of the method. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-21916-0_28 | ISMIS |
Keywords | Field | DocType |
preliminary feature construction phase,practical effectiveness,optimized context,logic clause,experimental session,knowledge base,complex multi-relational representation,genetic programming,clausal knowledge base,kernel function,semantic kernel,applicative domain | Data mining,Computer science,Description logic,Genetic programming,Natural language processing,Artificial intelligence,Knowledge base,Nearest neighbor search,Parameterized complexity,Deductive database,Kernel method,Machine learning,Kernel (statistics) | Conference |
Volume | ISSN | Citations |
6804 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 16 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicola Fanizzi | 1 | 1124 | 90.54 |
| Claudia D'Amato | 2 | 733 | 57.03 |