In GIS, mountains exist as a number of 0s and 1s. They may be stored in the hard disk as vectors, matrices, or even single values. By visualization, we extract those 0s and 1s from the storage, display them according to the user requirements, and label them as “mountains”. By this means, we admit that mountains exist physically in GIS research. But with ontology, which studies being or existence itself, it is quite hard to define what exactly a mountain is. By taking a look at the theories in geomorphology or hydrology, it is nearly impossible to find the starting and end of a mountain, and we can even challenge whether “mountain” is an appropriate name to describe the altitude of certain locations. But with information systems, ontology does not mainly deal with existence, but formalize the concepts under established logics or theories. To be more specific, in GIS, ontology helps us to clarify spatial information.
Let us get back to the “mountain” example in GIS. We need to give labels to most “mountains” a label for identification, such as the “Mont-Royal” on Google Maps. But is this label correct? What happened if we label it as “McGill Mountain” in another GIS? I think if we label it as “McGill Mountain”, someone can still recognize that mountain, at least most McGill students. But with ontology, we can easily figure out that “McGill Mountain” is equal to “Mont-Royal”, as they have the same feature in GIS.
One very interesting argument in the paper of Smith et al. 2003 is that they view environment modeling as field-based rather than object-based. But without objects, it is difficult to model filed itself. However, with ontology, the notion of “field” may be easier to conceptualize. But here comes the question: Does ontology differentiate with respect to the complexity of concepts?
–cyberinfrastructure
Tags: ontology