Foundational ontologies, ontology-driven conceptual modeling, and their multiple benefits to data mining

For many years, the role played by domain knowledge in all stages of knowledge discovery has been recognized. However, the real-world semantics embedded in data is often still not fully considered in traditional data mining methods. In this article, we argue that the quality of data mining results is directly related to the extent that they reflect important properties of real-world entities represented therein. Analyzing and characterizing the nature of these entities is the very business of the area of formal ontology. We briefly elaborate on two particular types of artifacts produced by this area: foundational ontologies and ontology-driven conceptual modeling languages grounded on them. We then elaborate on the benefits they can bring to several activities in a data mining process.

This article is categorized under:

Fundamental Concepts of Data and Knowledge > Knowledge Representation Fundamental Concepts of Data and Knowledge > Data Concepts.