A theory of diagnosis from first principles
Artificial Intelligence
Ontology Matching
The Description Logic Handbook
The Description Logic Handbook
Pellet: A practical OWL-DL reasoner
Web Semantics: Science, Services and Agents on the World Wide Web
Debugging Incoherent Terminologies
Journal of Automated Reasoning
ESWC '07 Proceedings of the 4th European conference on The Semantic Web: Research and Applications
Applying Logical Constraints to Ontology Matching
KI '07 Proceedings of the 30th annual German conference on Advances in Artificial Intelligence
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
A formal investigation of mapping language for terminological knowledge
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Finding all justifications of OWL DL entailments
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Using measures of semantic relatedness for word sense disambiguation
CICLing'03 Proceedings of the 4th international conference on Computational linguistics and intelligent text processing
Characterizing the semantic web on the web
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
Text2Onto: a framework for ontology learning and data-driven change discovery
NLDB'05 Proceedings of the 10th international conference on Natural Language Processing and Information Systems
Debugging and semantic clarification by pinpointing
ESWC'05 Proceedings of the Second European conference on The Semantic Web: research and Applications
Analysing Ontological Structures through Name Pattern Tracking
EKAW '08 Proceedings of the 16th international conference on Knowledge Engineering: Practice and Patterns
A Reasoning-Based Support Tool for Ontology Mapping Evaluation
ESWC 2009 Heraklion Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications
An Efficient Method for Computing Alignment Diagnoses
RR '09 Proceedings of the 3rd International Conference on Web Reasoning and Rule Systems
A Conflict-Based Operator for Mapping Revision
ISWC '09 Proceedings of the 8th International Semantic Web Conference
Dealing with matching variability of semantic web data using contexts
CAiSE'10 Proceedings of the 22nd international conference on Advanced information systems engineering
Ontology population and enrichment: state of the art
Knowledge-driven multimedia information extraction and ontology evolution
Inductive learning of disjointness axioms
OTM'11 Proceedings of the 2011th Confederated international conference on On the move to meaningful internet systems - Volume Part II
Graph-based ontology analysis in the linked open data
Proceedings of the 8th International Conference on Semantic Systems
Advocatus diaboli --- exploratory enrichment of ontologies with negative constraints
EKAW'12 Proceedings of the 18th international conference on Knowledge Engineering and Knowledge Management
Semantic precision and recall for evaluating incoherent ontology mappings
AMT'12 Proceedings of the 8th international conference on Active Media Technology
Data Linking for the Semantic Web
International Journal on Semantic Web & Information Systems
Computing incoherence explanations for learned ontologies
RR'13 Proceedings of the 7th international conference on Web Reasoning and Rule Systems
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Dealing with heterogeneous ontologies by means of semantic mappings has become an important area of research and a number of systems for discovering mappings between ontologies have been developed. Most of these systems rely on general heuristics for finding mappings, hence are bound to fail in many situations. Consequently, automatically generated mappings often contain logical inconsistencies that hinder a sensible use of these mappings. In previous work, we presented an approach for debugging mappings between expressive ontologies that eliminates inconsistencies by means of diagnostic reasoning. A shortcoming of this method was its need for expressive class definitions. More specifically, the applicability of this method critically relies on the existence of a high-quality disjointness axiomatization. This paper deals with the application of the debugging approach to mappings between lightweight ontologies that do not contain any or very few disjointness axioms, as it is the case for most of today's practical ontologies. After discussing different approaches to deal with the absence of disjointness axioms we propose the application of supervised machine learning for detecting disjointness in a fully automatic manner. We present a detailed evaluation of our approach to learning disjointness and its impact on mapping debugging. The results show that debugging automatically created mappings with the help of learned disjointness axioms significantly improves the overall quality of these mappings.