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OntoDM-KDD - Ontology for Data Mining Investigations

Background

The term knowledge discovery in databases, or KDD for short, refers to the broad process of finding knowledge in data. The term data mining refers to the sub-process of the KDD process that involves the application of algorithms for extraction of knowledge from data in form of patterns (generalizations). The KDD process is interactive and iterative. It involves numerous sub-processes, such as for example: developing an understanding of the application domain, the relevant prior knowledge, and end user's goals; collecting data and creating a target dataset; data cleaning and pre-processing; data reduction and projection; choice of data mining task; choice of data mining algorithm; modeling or data mining; deployment of mined knowledge; and incorporating and use of the mined knowledge.

OntoDM-KDD is a sub-ontology for representing data mining investigations. Its goal is to allow the representation of knowledge discovery processes and be general enough to represent the data mining investigations. In the domain of data mining and knowledge discovery, there are proposals for standardizing the process of knowledge discovery in the context of representing and performing data mining investigations. One of the most prominent proposals is the CRISP-DM methodology (Chapman et al., 1999). CRISP-DM stands for Cross Industry Standard Process for Data Mining. It is a process model that describes data mining investigations performed in practical applications. The CRISP-DM process model is based on commonly used approaches that expert data miners use to tackle and solve the practical problems in the domain of data mining. The OntoDM-KDD ontology is based on the CRISP-DM process model.

Content

The OntoDM-KDD ontology module provides:

  • a representation of data mining investigation by directly extending classes from the OBI and IAO ontologies;
  • a model of each of the phases (including their inputs and outputs) in a data mining investigation
    • application understanding,
    • data understanding,
    • data preparation,
    • modeling,
    • DM process evaluation, and
    • deployment.

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