Keet, CMLawrynowicz, AD'Amato, CHilario, M2014-05-062014-05-062013-05Keet, 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, Francehttp://webont.org/owled/2013/papers/owled2013_10.pdfhttp://hdl.handle.net/10204/73908th Workshop on OWL: Experiences and Directions (OWLED'13), 26-27 May 2013, Montpellier, FranceWe 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.enMeta-learningData Mining Optimization OntologyDMOPDMOP OntologyConference PresentationKeet, C., Lawrynowicz, A., D'Amato, C., & Hilario, M. (2013). Modeling issues & choices in the data mining optimization ontology. CRC Press. http://hdl.handle.net/10204/7390Keet, CM, A Lawrynowicz, C D'Amato, and M Hilario. "Modeling issues & choices in the data mining optimization ontology." (2013): http://hdl.handle.net/10204/7390Keet C, Lawrynowicz A, D'Amato C, Hilario M, Modeling issues & choices in the data mining optimization ontology; CRC Press; 2013. http://hdl.handle.net/10204/7390 .TY - Conference Presentation AU - Keet, CM AU - Lawrynowicz, A AU - D'Amato, C AU - Hilario, M AB - 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. DA - 2013-05 DB - ResearchSpace DP - CSIR KW - Meta-learning KW - Data Mining Optimization Ontology KW - DMOP KW - DMOP Ontology LK - https://researchspace.csir.co.za PY - 2013 T1 - Modeling issues & choices in the data mining optimization ontology TI - Modeling issues & choices in the data mining optimization ontology UR - http://hdl.handle.net/10204/7390 ER -