Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
On the Mining of Substitution Rules for Statistically Dependent Items
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Mining positive and negative association rules: an approach for confined rules
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
WWW '05 Proceedings of the 14th international conference on World Wide Web
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Pellet: A practical OWL-DL reasoner
Web Semantics: Science, Services and Agents on the World Wide Web
ESWC '07 Proceedings of the 4th European conference on The Semantic Web: Research and Applications
Learning Disjointness for Debugging Mappings between Lightweight Ontologies
EKAW '08 Proceedings of the 16th international conference on Knowledge Engineering: Practice and Patterns
A Kernel Revision Operator for Terminologies -- Algorithms and Evaluation
ISWC '08 Proceedings of the 7th International Conference on The Semantic Web
Ontology Learning and Reasoning -- Dealing with Uncertainty and Inconsistency
Uncertainty Reasoning for the Semantic Web I
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Completing description logic knowledge bases using formal concept analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
DBpedia - A crystallization point for the Web of Data
Web Semantics: Science, Services and Agents on the World Wide Web
Web Semantics: Science, Services and Agents on the World Wide Web
DL-Learner: Learning Concepts in Description Logics
The Journal of Machine Learning Research
Association rule mining: models and algorithms
Association rule mining: models and algorithms
ORE - a tool for repairing and enriching knowledge bases
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part II
ESWC'11 Proceedings of the 8th extended semantic web conference on The semantic web: research and applications - Volume Part I
Semantic Web
Creating knowledge out of interlinked data
Semantic Web
A framework for handling inconsistency in changing ontologies
ISWC'05 Proceedings of the 4th 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
DBpedia ontology enrichment for inconsistency detection
Proceedings of the 8th International Conference on Semantic Systems
Checking and handling inconsistency of DBpedia
WISM'12 Proceedings of the 2012 international conference on Web Information Systems and Mining
Advocatus diaboli --- exploratory enrichment of ontologies with negative constraints
EKAW'12 Proceedings of the 18th international conference on Knowledge Engineering and Knowledge Management
Universal OWL axiom enrichment for large knowledge bases
EKAW'12 Proceedings of the 18th international conference on Knowledge Engineering and Knowledge Management
Parameter learning for probabilistic ontologies
RR'13 Proceedings of the 7th international conference on Web Reasoning and Rule Systems
Integration of large scale knowledge bases using probabilistic graphical models
Proceedings of the 7th ACM international conference on Web search and data mining
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The tremendous amounts of linked data available on the web are a valuable resource for a variety of semantic applications. However, these applications often need to face the challenges posed by flawed or underspecified representations. The sheer size of these data sets, being one of their most appealing features, is at the same time a hurdle on the way towards more accurate data because this size and the dynamics of the data often hinder manual maintenance and quality assurance. Schemas or ontologies constraining, e.g., the possible instantiations of classes and properties, could facilitate the automated detection of undesired usage patterns or incorrect assertions, but only few knowledge repositories feature schema-level knowledge of sufficient expressivity. In this paper, we present several approaches to enriching learned or manually engineered ontologies with disjointness axioms, an important prerequisite for the applicability of logical approaches to knowledge base debugging. We describe the strengths and weaknesses of these approaches and report on a detailed evaluation based on the DBpedia dataset.