Probabilistic reasoning is an essential feature when dealing with many application domains. Starting with the idea that ontologies are the right way to formalize domain knowledge and that Bayesian networks are the right tool for probabilistic reasoning, we propose an approach for extracting a Bayesian network from a populated ontology and for reasoning over it. The paper presents the theory behind the approach, its design and examples of its use
Mining Bayesian networks out of ontologies
Bellandi A;Turini F
2012
Abstract
Probabilistic reasoning is an essential feature when dealing with many application domains. Starting with the idea that ontologies are the right way to formalize domain knowledge and that Bayesian networks are the right tool for probabilistic reasoning, we propose an approach for extracting a Bayesian network from a populated ontology and for reasoning over it. The paper presents the theory behind the approach, its design and examples of its useFile in questo prodotto:
| File | Dimensione | Formato | |
|---|---|---|---|
|
prod_217291-doc_50620.pdf
solo utenti autorizzati
Descrizione: Mining Bayesian networks out of ontologies
Tipologia:
Versione Editoriale (PDF)
Dimensione
869.95 kB
Formato
Adobe PDF
|
869.95 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
