Ontology Integration Using Mappings: Towards Getting the Right Logical Consequences
ESWC 2009 Heraklion Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications
The Relevance of Reasoning and Alignment Incoherence in Ontology Matching
ESWC 2009 Heraklion Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications
An intelligent query processing for distributed ontologies
Journal of Systems and Software
A Conflict-Based Operator for Mapping Revision
ISWC '09 Proceedings of the 8th International Semantic Web Conference
Save up to 99% of your time in mapping validation
OTM'10 Proceedings of the 2010 international conference on On the move to meaningful internet systems: Part II
Semantic recognition of ontology refactoring
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part I
Ontology and instance matching
Knowledge-driven multimedia information extraction and ontology evolution
LogMap: logic-based and scalable ontology matching
ISWC'11 Proceedings of the 10th international conference on The semantic web - Volume Part I
Semantic querying over knowledge in biomedical text corpora annotated with multiple ontologies
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Knowledge-Base revision using implications as hypotheses
KI'12 Proceedings of the 35th Annual German conference on Advances in Artificial Intelligence
Mapping adaptation actions for the automatic reconciliation of dynamic ontologies
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
RIO: minimizing user interaction in ontology debugging
RR'13 Proceedings of the 7th international conference on Web Reasoning and Rule Systems
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Finding correct semantic correspondences between heterogeneous ontologies is one of the most challenging problems in the area of semantic web technologies. As manually constructing such mappings is not feasible in realistic scenarios, a number of automatic matching tools have been developed that propose mappings based on general heuristics. As these heuristics often produce incorrect results, a manual revision is inevitable in order to guarantee the quality of generated mappings. Experiences with benchmarking matching systems revealed that the manual revision of mappings is still a very difficult problem because it has to take the semantics of the ontologies as well as interactions between mappings into account. In this article, we propose methods for supporting human experts in the task of revising automatically created mappings. In particular, we present non-standard reasoning methods for detecting and propagating implications of expert decisions on the correctness of a mapping.