Ontologies for Spatial Data Analytical Web Services

Ontologies for Spatial Data Analytical Web Services.

AAG Paper Session: Geospatial Semantic Web Technologies for Geospatial Interoperability

Saturday, 4/19/08 at 16:30 PM.
Author(s):
Myunghwa Hwang* (Myunghwa.Hwang@asu.edu) - GeoDaCenter at Arizona State University
Luc Anselin - School of Geographical Sciences at Arizona State University and GeoDa Center

Abstract:
Increasingly, the functionality of GIS and spatial analysis is
available over the internet, in the form of various web services. To
date, these are primarily geared at delivering data, providing mapping
functionality and supporting basic spatial queries. In this paper, we
focus on some technical aspects related to bringing exploratory spatial
data functionality to the geospatial semantic web. In addition to the
provision of the ESDA GIServices as such, an important aspect is the
preparation of an infrastructure that allows easy discovery of the web
service. This is approached by considering both syntactic and semantic
aspects of the description of user requests as the basis for the
development of an ontology for spatial processing services. We consider
the specific set of techniques pertaining to computing smoothed rates
since this is a relatively straightforward and well understood problem
which can be formalized relatively easily. The resulting ontology can
be viewed as a starting point towards developing a more comprehensive
infrastructure for spatial data analysis in general. Specifically, we
apply the METHONTOLOGY methodology and relate our work to existing
ontologies for generic GIS operations. The ontology is implemented in
Web Ontology Language (OWL) and tested using web services programmed in
Python and OWL-S (Semantic Markup for Web Services), SAWSDL (Semantic
Annotations for Web Services Description Language), and the Web Service
Modeling Ontology (WSMO) are compared as the framework to support
discovery. This is illustrated with a prototype application that allows
the computation of smoothed disease rates.

Keywords:
ontology, spatial data analysis, spatial data analytic web services,
semantic web services