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Inconsistency Repair to Improve the Performace of Ontology Matchers
the inconsistencies in ontologies before starting the matching process. In this phase, many problems caused by ontology diversity are resolved. Besides, some new patterns and inconsistencies in ontologies are detected and then refactoring operations are applied...
Using Pattern Detection Techniques and Refactoring to Improve the Performance of ASMOV
is Automated Semantic Matching of Ontologies with Verification, called ASMOV. In preprocessing phase, some new patterns of ontologies are detected and then refactoring operations are used for reaching assimilated ontologies. Afterward, we applied ASMOV...
Adding a Pre processing Phase to ASMOV for Improving the Alignment Result
can improve the results of the matching process. A pre-processing phase is added to the matchers for the analysis of input ontologies so as to detect some inappropriate patterns which are modeled by various developers. Then, refactoring operations...
Random projection based k Nearest Neighbor rule for semiconductor process fault detection
Quality-centric security pattern mutations
pattern mutations which are created by mutating current patterns using design refactoring rules. These mutations offer the same behavior as the initial pattern but with varying effects on quality attributes such as flexibility, reusability, extendibility...
The Impact of Refactoring Based on Naming Pattern Detection on RiMOM Result
dynamic multi-strategy ontology
alignment framework, called RiMOM and add the
preprocessing phase to it. In preprocessing phase, some
different patterns of ontologies are detected and then
refactoring rules are used for uniform...
Using Pattern Detection Techniques and Refactoring to Improve the Performance of ASMOV
is Automated Semantic Matching of Ontologies with Verification, called ASMOV. In preprocessing phase, some new patterns of ontologies are detected and then refactoring operations are used for reaching assimilated ontologies. Afterward, we applied ASMOV...