Enhancing the Process of Knowledge Discovery in Geographic Databases Using Geo-Ontologies

Abstract

This chapter introduces the problem of mining frequent geographic patterns and spatial association rules from geographic databases. In the geographic domain most discovered patterns are trivial, non-novel, and noninteresting, which simply represent natural geographic associations intrinsic to geographic data. A large amount of natural geographic associations are explicitly represented in geographic database schemas and geo-ontologies, which have not been used so far in frequent geographic pattern mining. Therefore, this chapter presents a novel approach to extract patterns from geographic databases using geoontologies as prior knowledge. The main goal of this chapter is to show how the large amount of knowledge represented in geo-ontologies can be used to avoid the extraction of patterns that are previously known as noninteresting.