Name
Abstract | ||
|---|---|---|
We describe a method for learning functions that can predict the ranking of resources in knowledge bases expressed in Description Logics. The method relies on a kernelized version of the PERCEPTRON RANKING algorithm which is suitable for batch but also online problems settings. The usage of specific kernel functions that encode the similarity between individuals in the context of knowledge bases allows the application of the method to ontologies in the standard representations for the Semantic Web. An extensive experimentation reported in this paper proves the effectiveness of the method at the task of ranking the answers to queries, expressed by class descriptions when applied to real ontologies describing simple and complex domains. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-15918-3_14 | RR |
Keywords | Field | DocType |
perceptron ranking algorithm,real ontology,class description,online problems setting,kernelized version,knowledge base,semantic web,extensive experimentation,description logics,kernel perceptrons,complex domain,description logic,kernel function,learning to rank | Learning to rank,Data mining,Ranking SVM,Computer science,Semantic Web,Description logic,Theoretical computer science,Artificial intelligence,Ontology (information science),Ranking,Perceptron,Machine learning,Kernel (statistics) | Conference |
Volume | ISSN | ISBN |
6333 | 0302-9743 | 3-642-15917-6 |
Citations | PageRank | References |
3 | 0.46 | 13 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicola Fanizzi | 1 | 1124 | 90.54 |
| Claudia D'Amato | 2 | 733 | 57.03 |
| Floriana Esposito | 3 | 2434 | 277.96 |