Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
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
Supporting manual mapping revision using logical reasoning
AAAI'08 Proceedings of the 23rd 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
Non-standard reasoning services for the debugging of description logic terminologies
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Exploiting Partial Information in Taxonomy Construction
ISWC '09 Proceedings of the 8th International Semantic Web Conference
Semi-Automatic Revision of Formalized Knowledge
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Wheat and chaff - practically feasible interactive ontology revision
ISWC'11 Proceedings of the 10th international conference on The semantic web - Volume Part I
Reasoning-supported interactive revision of knowledge bases
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Advocatus diaboli --- exploratory enrichment of ontologies with negative constraints
EKAW'12 Proceedings of the 18th international conference on Knowledge Engineering and 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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When ontological knowledge is acquired automatically, quality control is essential. Which part of the automatically acquired knowledge is appropriate for an application often depends on the context in which the knowledge base or ontology is used. In order to determine relevant and irrelevant or even wrong knowledge, we support the tightest possible quality assurance approach - an exhaustive manual inspection of the acquired data. By using automated reasoning, this process can be partially automatized: after each expert decision, axioms that are entailed by the already confirmed statements are automatically approved, whereas axioms that would lead to an inconsistency are declined. Starting from this consideration, this paper provides theoretical foundations, heuristics, optimization strategies and comprehensive experimental results for our approach to efficient reasoning-supported interactive ontology revision. We introduce and elaborate on the notions of revision states and revision closure as formal foundations of our method. Additionally, we propose a notion of axiom impact which is used to determine a beneficial order of axiom evaluation in order to further increase the effectiveness of ontology revision. The initial notion of impact is then further refined to take different validity ratios - the proportion of valid statements within a dataset - into account. Since the validity ratio is generally not known a priori - we show how one can work with an estimate that is continuously improved over the course of the inspection process. Finally, we develop the notion of decision spaces, which are structures for calculating and updating the revision closure and axiom impact. We optimize the computation performance further by employing partitioning techniques and provide an implementation supporting these optimizations as well as featuring a user front-end. Our evaluation shows that our ranking functions almost achieve the maximum possible automatization and that the computation time needed for each reasoning-based, automatic decision takes less than one second on average for our test dataset of over 25000 statements.