A Polynomial Approach to the Constructive Induction of Structural Knowledge
Machine Learning - Special issue on evaluating and changing representation
Kernels and Distances for Structured Data
Machine Learning
Kernel methods for mining instance data in ontologies
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Query answering and ontology population: an inductive approach
ESWC'08 Proceedings of the 5th European semantic web conference on The semantic web: research and applications
A refinement operator based learning algorithm for the ALC description logic
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
A declarative kernel for concept descriptions
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Relational kernel machines for learning from graph-structured RDF data
ESWC'11 Proceedings of the 8th extended semantic web conference on The semantic web: research and applications - Volume Part I
Learning with semantic kernels for clausal knowledge bases
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Induction of robust classifiers for web ontologies through kernel machines
Web Semantics: Science, Services and Agents on the World Wide Web
Data Mining and Knowledge Discovery
Learning probabilistic Description logic concepts: under different Assumptions on missing knowledge
Proceedings of the 27th Annual ACM Symposium on Applied Computing
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We tackle the problem of statistical learning in the standard knowledge base representations for the Semantic Web which are ultimately expressed in description Logics. Specifically, in our method a kernel functions for the $\mathcal{ALCN}$ logic integrates with a support vector machine which enables the usage of statistical learning with reference representations. Experiments where performed in which kernel classification is applied to the tasks of resource retrieval and query answering on OWL ontologies.