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.
Reference:
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
Keet, C., Lawrynowicz, A., D'Amato, C., & Hilario, M. (2013). Modeling issues & choices in the data mining optimization ontology. CRC Press. http://hdl.handle.net/10204/7390
Keet, CM, A Lawrynowicz, C D'Amato, and M Hilario. "Modeling issues & choices in the data mining optimization ontology." (2013): http://hdl.handle.net/10204/7390
Keet 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 .