Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Ranking Complex Relationships on the Semantic Web
IEEE Internet Computing
Variable-strength conditional preferences for ranking objects in ontologies
Web Semantics: Science, Services and Agents on the World Wide Web
Statistical Learning for Inductive Query Answering on OWL Ontologies
ISWC '08 Proceedings of the 7th International Conference on The Semantic Web
Semantic matchmaking as non-monotonic reasoning: a description logic approach
Journal of Artificial Intelligence Research
TripleRank: Ranking Semantic Web Data by Tensor Decomposition
ISWC '09 Proceedings of the 8th International Semantic Web Conference
Towards Learning to Rank in Description Logics
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Towards top-k query answering in description logics: the case of DL-Lite
JELIA'06 Proceedings of the 10th European conference on Logics in Artificial Intelligence
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We describe a method for learning functions that can predict the ranking of resources in knowledge bases expressed in Description Logics. The method relies on a kernelized version of the PERCEPTRON RANKING algorithm which is suitable for batch but also online problems settings. The usage of specific kernel functions that encode the similarity between individuals in the context of knowledge bases allows the application of the method to ontologies in the standard representations for the Semantic Web. An extensive experimentation reported in this paper proves the effectiveness of the method at the task of ranking the answers to queries, expressed by class descriptions when applied to real ontologies describing simple and complex domains.