On the Dempster-Shafer framework and new combination rules
Information Sciences: an International Journal
International Journal of Approximate Reasoning
Ontology Matching
The Description Logic Handbook
The Description Logic Handbook
On the Influence of Description Logics Ontologies on Conceptual Similarity
EKAW '08 Proceedings of the 16th international conference on Knowledge Engineering: Practice and Patterns
Statistical Learning for Inductive Query Answering on OWL Ontologies
ISWC '08 Proceedings of the 7th International Conference on The Semantic Web
Using the Dempster-Shafer Theory of Evidence to Resolve ABox Inconsistencies
Uncertainty Reasoning for the Semantic Web I
Analogical Reasoning in Description Logics
Uncertainty Reasoning for the Semantic Web I
Approximate Measures of Semantic Dissimilarity under Uncertainty
Uncertainty Reasoning for the Semantic Web I
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
An Evidence-Theoretic k-Nearest Neighbor Rule for Multi-label Classification
SUM '09 Proceedings of the 3rd International Conference on Scalable Uncertainty Management
Completing description logic knowledge bases using formal concept analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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
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In the line of our investigation of inductive methods for Semantic Web reasoning, we propose an alternative way for approximate ABox reasoning based on the evidence and the analogical principle of the nearest-neighbors. Once neighbors of a test individual are selected through some distance measures, a combination rule descending from the Dempster-Shafer theory can join together the evidence provided by the various neighbor individuals in order to predict unknown values in a learning problem. We show how to exploit the procedure in the problems of determining unknown class- and role-memberships or fillers for datatype properties which may be the basis for many further ABox inductive reasoning algorithms.