The increasing availability of structured machine-processable knowl- edge in the W EB OF D ATA calls for machine learning methods to support stan- dard pattern matching and reasoning based services (such as query-answering and inference). Statistical regularities can be efficiently exploited to overcome the limitations of the inherently incomplete knowledge bases distributed across the Web. This paper focuses on the problem of predicting missing class-memberships and property values of individual resources in Web ontologies. We propose a transductive inference method for inferring missing properties about individuals: given a class-membership/property value prediction problem, we address the task of identifying relations encoding similarities between individuals, and efficiently propagating knowledge across their relations
Learning to Propagate Knowledge in Web Ontologies
D'AMATO, CLAUDIA;FANIZZI, Nicola;
2014-01-01
Abstract
The increasing availability of structured machine-processable knowl- edge in the W EB OF D ATA calls for machine learning methods to support stan- dard pattern matching and reasoning based services (such as query-answering and inference). Statistical regularities can be efficiently exploited to overcome the limitations of the inherently incomplete knowledge bases distributed across the Web. This paper focuses on the problem of predicting missing class-memberships and property values of individual resources in Web ontologies. We propose a transductive inference method for inferring missing properties about individuals: given a class-membership/property value prediction problem, we address the task of identifying relations encoding similarities between individuals, and efficiently propagating knowledge across their relationsI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
