Factorizing YAGO: scalable machine learning for linked data
Proceedings of the 21st international conference on World Wide Web
Combining information extraction, deductive reasoning and machine learning for relation prediction
ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
Scalable relation prediction exploiting both intrarelational correlation and contextual information
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
| Hi-index | 0.00 |
This work concerns non-parametric approaches for statistical learning applied to the standard knowledge representations languages adopted in the Semantic Web context. We present methods based on epistemic inference that are able to elicit the semantic similarity of individuals in OWL knowledge bases. Specifically, a totally semantic and language independent semi-distance function is presented and from it, an epistemic kernel function for Semantic Web representations is derived. Both the measure and the kernel function are embedded into non-parametric statistical learning algorithms customized for coping with Semantic Web representations. Particularly, the measure is embedded into a k-Nearest Neighbor algorithm and the kernel function is embedded in a Support Vector Machine. The realized algorithms are used to performe inductive concept retrieval and query answering. An experimentation on real ontologies proves that the methods can be effectively employed for performing the target tasks and moreover that it is possible to induce new assertions that are not logically derivable.