ACM Computing Surveys (CSUR)
Combining belief functions when evidence conflicts
Decision Support Systems
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Distance Induction in First Order Logic
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
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
Evolutionary Conceptual Clustering of Semantically Annotated Resources
ICSC '07 Proceedings of the International Conference on Semantic Computing
Instance-Based Query Answering with Semantic Knowledge Bases
AI*IA '07 Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing
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
Decentralized case-based reasoning for the semantic web
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
Prediction of class and property assertions on OWL ontologies through evidence combination
Proceedings of the International Conference on Web Intelligence, Mining and Semantics
Induction of robust classifiers for web ontologies through kernel machines
Web Semantics: Science, Services and Agents on the World Wide Web
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We propose semantic distance measures based on the criterion of approximate discernibility and on evidence combination. In the presence of incomplete knowledge, the distance functions measure the degree of belief in the discernibility of two individuals by combining estimates of basic probability masses related to a set of discriminating features. We also suggest ways to extend this distance for comparing individuals to concepts and concepts to other concepts. Integrated within a k-Nearest Neighbor algorithm, the measures have been experimentally tested on a task of inductive concept retrieval demonstrating the effectiveness of their application.