Domain-Oriented Multilevel Ontology for Adaptive Data Processing

Abstract

In the data mining domain, the diversity of algorithms and the clutter of data make the knowledge discovery process very unfriendly to many non-computer professional researchers. Meta-learning helps users to modify some aspects of this process to improve the performance of the resulting model. Semantic meta mining is the process of mining metadata about data mining algorithms based on expertise extracted from the knowledge base. The knowledge base is usually represented in the form of ontology. This article proposes a domain-oriented multi-level ontology (DoMO) through merging and improving existing data mining ontologies. It provides the restrictions of the dataset characteristics to help the domain experts describe data set in the form of ontology entities. According to the entities of the data characteristics in DoMO, the users can query the ontology to obtain the optimized data processing process. In this paper, we take the time series classification problem as an example to present the effectiveness of the proposed ontology.

A review of data mining ontologies

“…Arantes and Yokome (2011) briefly described OntoDM (Panov et al , 2008), Expose (Vanschoren and Soldatova, 2010), Data Mining Ontology 3 (DMO 3 ) (Zheng and Li, 2008) and Data Mining Ontology 2 (DMO 2; Brezany et al , 2008). Tianxing et al (2020) discussed DMOP, OntoDM, Ontology for Generic Datatypes (OntoDT; Panov et al , 2013), Knowledge Discovery Ontology (KDO) Zakova et al (2008), KDDONTO and DMWF ( Kietz et al , 2010) in brief. Similarly, Benali and Rahal (2017) discussed the Meta-Data Mining Ontology (Meta-DM; Arantes and Yokome, 2011), OntoDT, OntoDM, DMOP, Expose and Ontology for Data Mining Entities for Knowledge Discovery in Databases (OntoDM-KDD) (Panov et al , 2013) in brief.…”