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dc.contributor.author Keet, CM
dc.contributor.author Lawrynowicz, A
dc.contributor.author D'Amato, C
dc.contributor.author Hilario, M
dc.date.accessioned 2014-05-06T12:33:34Z
dc.date.available 2014-05-06T12:33:34Z
dc.date.issued 2013-05
dc.identifier.citation Keet, C.M, Lawrynowicz, A, d’Amato, C and Hilario, M. 2013. Modeling issues & choices in the data mining optimization ontology. In: 8th Workshop on OWL: Experiences and Directions (OWLED'13), 26-27 May 2013, Montpellier, France en_US
dc.identifier.uri http://webont.org/owled/2013/papers/owled2013_10.pdf
dc.identifier.uri http://hdl.handle.net/10204/7390
dc.description 8th Workshop on OWL: Experiences and Directions (OWLED'13), 26-27 May 2013, Montpellier, France en_US
dc.description.abstract We describe the Data Mining Optimization Ontology (DMOP), which was developed to support informed decision-making at various choice points of the knowledge discovery (KD) process. It can be used as a reference by data miners, but its primary purpose is to automate algorithm and model selection through semantic meta-mining, i.e., ontology-based meta-analysis of complete data mining processes in view of extracting patterns associated with mining performance. DMOP contains in-depth descriptions of DM tasks (e.g., learning, feature selection), data, algorithms, hypotheses (mined models or patterns), and workflows. Its development raised a number of non-trivial modeling problems, the solution to which demanded maximal exploitation of OWL 2 representational potential. We discuss a number of modeling issues encountered and the choices made that led to version 5.3 of the DMOP ontology. en_US
dc.language.iso en en_US
dc.publisher CRC Press en_US
dc.relation.ispartofseries Workflow;12377
dc.subject Meta-learning en_US
dc.subject Data Mining Optimization Ontology en_US
dc.subject DMOP en_US
dc.subject DMOP Ontology en_US
dc.title Modeling issues & choices in the data mining optimization ontology en_US
dc.type Presentation en_US


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