Making large-scale support vector machine learning practical
Advances in kernel methods
Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
The description logic handbook: theory, implementation, and applications
The description logic handbook: theory, implementation, and applications
Text classification using string kernels
The Journal of Machine Learning Research
A survey of kernels for structured data
ACM SIGKDD Explorations Newsletter
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Kernels and Distances for Structured Data
Machine Learning
Kernels on Prolog Proof Trees: Statistical Learning in the ILP Setting
The Journal of Machine Learning Research
Handbook on Ontologies
The SWRC ontology – semantic web for research communities
EPIA'05 Proceedings of the 12th Portuguese conference on Progress in Artificial Intelligence
Classification and Retrieval through Semantic Kernels
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III
Learning with Kernels in Description Logics
ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
Statistical Learning for Inductive Query Answering on OWL Ontologies
ISWC '08 Proceedings of the 7th International Conference on The Semantic Web
ReduCE: A Reduced Coulomb Energy Network Method for Approximate Classification
ESWC 2009 Heraklion Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications
Metric-based stochastic conceptual clustering for ontologies
Information Systems
Metric-based stochastic conceptual clustering for ontologies
Information Systems
Connectionist Models for Formal Knowledge Adaptation
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Adding data mining support to SPARQL via statistical relational learning methods
ESWC'08 Proceedings of the 5th European semantic web conference on The semantic web: research and applications
Mining association rules from semantic web data
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
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
Finding association rules in semantic web data
Knowledge-Based Systems
Application and evaluation of inductive reasoning methods for the semantic web and software analysis
RW'11 Proceedings of the 7th international conference on Reasoning web: semantic technologies for the web of data
ASPARAGUS - a system for automatic SPARQL query results aggregation using semantics
ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part I
Induction of robust classifiers for web ontologies through kernel machines
Web Semantics: Science, Services and Agents on the World Wide Web
ICCBR'11 Proceedings of the 19th international conference on Case-Based Reasoning Research and Development
Factorizing YAGO: scalable machine learning for linked data
Proceedings of the 21st international conference on World Wide Web
Data Mining and Knowledge Discovery
ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
Rank prediction for semantically annotated resources
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Predicting knowledge in an ontology stream
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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The amount of ontologies and meta data available on the Web is constantly growing. The successful application of machine learning techniques for learning of ontologies from textual data, i.e. mining for the Semantic Web, contributes to this trend. However, no principal approaches exist so far for mining from the Semantic Web. We investigate how machine learning algorithms can be made amenable for directly taking advantage of the rich knowledge expressed in ontologies and associated instance data. Kernel methods have been successfully employed in various learning tasks and provide a clean framework for interfacing between non-vectorial data and machine learning algorithms. In this spirit, we express the problem of mining instances in ontologies as the problem of defining valid corresponding kernels. We present a principled framework for designing such kernels by means of decomposing the kernel computation into specialized kernels for selected characteristics of an ontology which can be flexibly assembled and tuned. Initial experiments on real world Semantic Web data enjoy promising results and show the usefulness of our approach.