Automated geographic information fusion and ontology alignment

Geographic information fusion is the process of integrating geographic information from diverse sources to produce new information with added value, reliability, or usefulness (cf. [14,67]). Geographic information fusion is an important function of interoperable and web-based GIS. Increased reliance on distributed web-based access to geographic information is correspondingly increasing the need to efficiently and rapidly fuse geographic information from multiple sources. The overriding problem facing any geographic information fusion system is semantic heterogeneity , where the concepts and categories used in different geographic information sources have incompatible meanings. Most of today’s geographic information fusion techniques are fundamentally dependent on human domain expertise. This chapter examines the foundations of automated geographic information fusion using inductive inference. Inductive inference concerns reasoning from specific cases to general rules. In the context of geographic information fusion, inductive inference can be used to infer semantic relationships between categories of geographic entities (general rules) from the spatial relationships between sets of specific entities. However, inductive inference is inherently unreliable, especially in the presence of uncertainty. Consequently, managing reliability is a key hurdle facing any automated fusion system based on inductive inference, especially in the domain of geographic information where uncertainty is endemic. This chapter develops a model of automated geographic information fusion based on inductive inference. Central to this model are techniques by which unreliable inferences and data can be accommodated. The key contributions of this chapter are to: