Modeling issues and choices in the Data Mining OPtimization Ontology
The authors 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 and 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.