Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Data Mining
The Description Logic Handbook
The Description Logic Handbook
Expressive probabilistic description logics
Artificial Intelligence
DL-FOIL Concept Learning in Description Logics
ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
Approximate OWL-Reasoning with Screech
RR '08 Proceedings of the 2nd International Conference on Web Reasoning and Rule Systems
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
Completing description logic knowledge bases using formal concept analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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
Scalable instance retrieval for the semantic web by approximation
WISE'05 Proceedings of the 2005 international conference on Web Information Systems Engineering
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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In order to overcome the limitations of purely deductive approaches to the tasks of classification and retrieval from ontologies, inductive (instance-based) methods have been proposed as efficient and noise-tolerant alternative. In this paper we propose an original method based on non-parametric learning: the Reduced Coulomb Energy (RCE) Network. The method requires a limited training effort but it turns out to be very effective during the classification phase. Casting retrieval as the problem of assessing the class-membership of individuals w.r.t. the query concepts, we propose an extension of a classification algorithm using RCE networks based on an entropic similarity measure for OWL. Experimentally we show that the performance of the resulting inductive classifier is comparable with the one of a standard reasoner and often more efficient than with other inductive approaches. Moreover, we show that new knowledge (not logically derivable) is induced and the likelihood of the answers may be provided.