Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Kernels and Distances for Structured Data
Machine Learning
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Ontology Matching
The Description Logic Handbook
The Description Logic Handbook
An algorithm based on counterfactuals for concept learning in the Semantic Web
Applied Intelligence
Kernels on Prolog Proof Trees: Statistical Learning in the ILP Setting
The Journal of Machine Learning Research
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Randomized metric induction and evolutionary conceptual clustering for semantic knowledge bases
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Learning with Kernels in Description Logics
ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
Using Semantic Distances for Reasoning with Inconsistent Ontologies
ISWC '08 Proceedings of the 7th International Conference on The Semantic Web
Statistical Learning for Inductive Query Answering on OWL Ontologies
ISWC '08 Proceedings of the 7th International Conference on The Semantic Web
Just Add Weights: Markov Logic for the Semantic Web
Uncertainty Reasoning for the Semantic Web I
Approximate Measures of Semantic Dissimilarity under Uncertainty
Uncertainty Reasoning for the Semantic Web I
Perspectives of Neural-Symbolic Integration
Perspectives of Neural-Symbolic Integration
Realizing Default Logic over Description Logic Knowledge Bases
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Concept learning in description logics using refinement operators
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 framework for handling inconsistency in changing ontologies
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
Resolution-Based approximate reasoning for OWL DL
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
A declarative kernel for concept descriptions
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Repairing unsatisfiable concepts in OWL ontologies
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
Support vector inductive logic programming
DS'05 Proceedings of the 8th international conference on Discovery Science
Rank prediction for semantically annotated resources
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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The paper focuses on the task of approximate classification of semantically annotated individual resources in ontological knowledge bases. The method is based on classification models built through kernel methods, a well-known class of effective statistical learning algorithms. Kernel functions encode a notion of similarity among elements of some input space. The definition of a family of parametric language-independent kernel functions for individuals occurring in an ontology allows the application of these statistical learning methods on Semantic Web knowledge bases. The classification models induced by kernel methods offer an alternative way to classify individuals with respect to the typical exact and approximate deductive reasoning procedures. The proposed statistical setting enables further inductive approaches to a variety of other tasks that can better cope with the inherent incompleteness of the knowledge bases in the Semantic Web and with their potential incoherence due to their distributed nature. The effectiveness of the proposed method is empirically proved through experiments on the task of approximate classification with real ontologies collected from standard repositories.