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ReduCE: A Reduced Coulomb Energy Network Method for Approximate Classification Dipartimento di Informatica Università degli studi di Bari Nicola Fanizzi Claudia d'Amato Floriana Esposito
Table of Contents ,[object Object]
Learning RCE Networks
Approximate Classifications of Individuals
Experiments
Conclusions & Outlook
Motivation ,[object Object]
implicit models: neural networks, support vector machines, graphic probabilistic models ,[object Object],[object Object]
enables approximation
better exploitation of the inherently incomplete information in Kbs for specific tasks
Applications of Inductive Models ,[object Object]
This can be also exploited for ,[object Object]
subsumption
... ,[object Object]
Ultimately, may be used for completing ontologies with probabilistic assertions ,[object Object]
Learning Problem ,[object Object]
Train a model (hypothesis) h Q using: ,[object Object]
A knowledge base K as background knowledge ,[object Object]
Use the learned model to classify all other individuals: ,[object Object]
Output h Q ( x 0 ) and possibly the likelihood of this assertion

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