Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Efficient Data Mining Based on Formal Concept Analysis
DEXA '02 Proceedings of the 13th International Conference on Database and Expert Systems Applications
Axiom Pinpointing in General Tableaux
TABLEAUX '07 Proceedings of the 16th international conference on Automated Reasoning with Analytic Tableaux and Related Methods
Exploring Finite Models in the Description Logic ${\mathcal {EL}}_{\rm gfp}$
ICFCA '09 Proceedings of the 7th International Conference on Formal Concept Analysis
Completing description logic knowledge bases using formal concept analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Terminological cycles in a description logic with existential restrictions
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
DBpedia - A crystallization point for the Web of Data
Web Semantics: Science, Services and Agents on the World Wide Web
A finite basis for the set of ƐL-implications holding in a finite model
ICFCA'08 Proceedings of the 6th international conference on Formal concept analysis
ESWC'11 Proceedings of the 8th extended semantic web conference on The semantic web: research and applications - Volume Part I
Two basic algorithms in concept analysis
ICFCA'10 Proceedings of the 8th international conference on Formal Concept Analysis
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In a recent approach, Baader and Distel proposed an algorithm to axiomatize all terminological knowledge that is valid in a given data set and is expressible in the description logic ELK. This approach is based on the mathematical theory of formal concept analysis. However, this algorithm requires the initial data set to be free of errors, an assumption that normally cannot be made for real-world data. In this work, we propose a first extension of the work of Baader and Distel to handle errors in the data set. The approach we present here is based on the notion of confidence, as it has been developed and used in the area of data mining.