## 1 Towards Semantic GIS
### Understanding Semantics of GIS  
Emerging applications involving GIS introduce strong demands in terms of performance, capabilities and interoperability affecting most aspects of the GIS's functional and data infrastructure. Most existing GIS are being re-designed and re-packaged to address extended requirements, as well as in an attempt to exploit or explore market opportunities as much as possible.  
This tendency to re-engineering, apart from not always being effective, sometimes causes some confusion in a field already characterized by a great diversity of applications. Furthermore, the inability to accommodate emerging requirements into current models is quite evident in most cases (e.g. [17, 18]). The feeling is that models should evolve in accordance with technological environments.  
In fact, GIS are integrating systems which bring together knowledge and ideas developed in many different areas, as well as knowledge-intensive processes, complex data models and patterns. In this context, most solutions look for a heuristic response to specific problems rather than tangible advances. It is not always easy to evaluate the impact of _ad-hoc_ solutions for a wider range of applications and to generalize application-oriented approaches.  
Furthermore, there are many different ways to define and classify subjects, objects and relations between them. The target knowledge often exists with multiple perspectives of the information; rich data models [19] are required to represent complex information fully supporting further steps for business processes (e.g., analysis). Heterogeneous models from different domains are being produced, but it is difficult to achieve a convergence process.  
Such considerations, and the constantly increasing systems' scale, are leading researchers to the progressive integration of semantic models and, consequently, to a design approach oriented to the semantic web. The benefits and current limitations of semantics are extensively discussed in the literature (e.g., in [8]) but are not the object of discussion in the context of this paper.  
It is possible to identify a wide range of different approaches in order to apply semantic models to GIS. Such solutions would consistently vary in function of the context and the purpose(s). For example, current research solutions can be designed according to an overall approach (e.g., semantic geospatial web [13]) or could match a completely vertical approach. Hybrid approaches, based on interdependent semantic layers, are currently in development in the context of several research projects.  
An analysis of semantic needs for GIS architectures identifies at least two different kinds of semantic support:  
* _Horizontal or Base support_ mostly refers to the geographic space itself. Current models fundamentally lack formalization which makes finding a high-level match, or definition of consistent relations among the different components, difficult. By providing a rich and formalized set of concepts, an ontological approach (e.g., [20, 21]) could provide a revolutionary solution for problems of geographical information modeling, enabling semantically interoperable frameworks, spatial data reuse, data sharing and mining, as well as the development of intelligent networks.
* _Vertical support_ is a natural complement to the previous one since it should basically define a formal interoperable meta-layer for the specification of data layers and relevant relations. Such data structures should overcome the barriers affecting the internal management of content inside the GIS, as well as enabling a consistent data sharing model [22].