Name
Abstract | ||
|---|---|---|
In this article, we present an ontology for representing the knowledge discovery (KD) process based on the CRISP-DM process model (OntoDM-KDD). OntoDM-KDD defines the most essential entities for describing data mining investigations in the context of KD in a two-layered ontological structure. The ontology is aligned and reuses state-of-the-art resources for representing scientific investigations, such as Information Artifact Ontology (IAO) and Ontology of Biomedical Investigations (OBI). It provides a taxonomy of KD specific actions, processes and specifications of inputs and outputs. OntoDM-KDD supports the annotation of DM investigations in application domains. The ontology has been thoroughly assessed following the best practices in ontology engineering, is fully interoperable with many domain resources and easily extensible. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-40897-7_9 | DISCOVERY SCIENCE |
Keywords | Field | DocType |
Knowledge Discovery in Databases, CRISP-DM, Data Mining Investigation, Data Mining, Domain Ontology | Ontology (information science),Ontology engineering,Data mining,Ontology-based data integration,Information retrieval,Process ontology,Open Biomedical Ontologies,Computer science,Ontology Inference Layer,Suggested Upper Merged Ontology,Upper ontology | Conference |
Volume | ISSN | Citations |
8140 | 0302-9743 | 10 |
PageRank | References | Authors |
0.69 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pance Panov | 1 | 38 | 7.90 |
| Larisa N. Soldatova | 2 | 180 | 20.75 |
| Saso Dzeroski | 3 | 1906 | 582.54 |