In the preprocessing course of spatial data, different departments always have diverse naming methods when describing
the same geographical entity, due to different backgrounds and views of angle. There is also great difference among the
feature sets which are used to describe concepts of geo-ontology, making it difficult to conduct semantic interoperation
based on the theory of concepts reasoning in the information science. Consequently, this paper takes green land system
for example and presents a reasoning method of geo-ontology based on object-oriented remote sensing analysis. We
firstly establish an image hierarchical network system by using the object-oriented multi-scale segmentation technology.
Then, the mapping from domain ontologies to image objects is realized by the maximum area method. Finally, through
analyzing the features of image objects, the reasoning principles are built up, realizing the semantic interoperation
between concepts of ontologies and image objects.
According to the heterogeneous and spatial characteristic of environmental disaster events, this paper uses wetland information distributed in different administrative departments of Wuhan as research objects, attempts to construct an integrated and interoperation system based on semantic grid to tackle with the sudden environmental disaster. Through the conversion from domain (node) ontologies to universal ontologies in the spatial information semantic grids of environmental disaster, we can resolve the distribution and heterogeneity problem of the spatial information about environmental disaster, and logically provide users a virtual single spatial information view. With the management, registration and service mechanism of environmental disaster information and resource based on geographic ontology, all the operation are based on semantic. It can implement the grid calculation based on semantic and the integration and interoperation of environmental disaster spatial information and resources. As the instance shows that, the system can settle the heterogeneity problem of various GISs in a certain extent, and facilitate the semantic integration and interoperation among various systems.
For meeting the challenge of multi-scale and uncertainty of spatial data, a new theory of building Geo-ontology, which is
based on Stratified Rough Sets, is put forward in this paper. The theory for building Geo-ontology based on Stratified
Rough Set, called GOSR, studies the Geo-ontology from two aspects: the intension and the extension of the ontology
concept.By extending the only one equivalence relation of the rough set to more than two equivalence relations, we
consider a nested sequence of m equivalence relations:
E1 &subuline; E2 &subuline; . . . &subuline; Em.
In conclusion, Geo-Ontology based on Stratified Rough Sets has these characters: a series nested equivalence relation
forms a granule different partial ordered lattice. An equivalence relation corresponds to a universe. The elements in the
universe are the rough objects in the same spatial scale. The lowest universe is built up by the atom spatial objects. The
elements in the higher universe can be built up by generalizability or combination of the lower universe. In this way, we
can associate the universes of the Stratified Rough Sets with the multi-scale, uncertain spatial data. On the other hand,
the equivalence classes defined by different equivalence relations correspond to different semantic granular
Geo-Ontology conception, describing the semantic intension of the spatial data.
Analyzing the characteristic of multi-Agent and geographic Ontology, The concept of the Agent-based Spatial Information Semantic Grid (ASISG) is defined and the architecture of the ASISG is advanced. ASISG is composed with Multi-Agents and geographic Ontology. The Multi-Agent Systems are composed with User Agents, General Ontology Agent, Geo-Agents, Broker Agents, Resource Agents, Spatial Data Analysis Agents, Spatial Data Access Agents, Task Execution Agent and Monitor Agent. The architecture of ASISG have three layers, they are the fabric layer, the grid management layer and the application layer. The fabric layer what is composed with Data Access Agent, Resource Agent and Geo-Agent encapsulates the data of spatial information system so that exhibits a conceptual interface for the Grid management layer. The Grid management layer, which is composed with General Ontology Agent, Task Execution Agent and Monitor Agent and Data Analysis Agent, used a hybrid method to manage all resources that were registered in a General Ontology Agent that is described by a General Ontology System. The hybrid method is assembled by resource dissemination and resource discovery. The resource dissemination push resource from Local Ontology Agent to General Ontology Agent and the resource discovery pull resource from the General Ontology Agent to Local Ontology Agents. The Local Ontology Agent is derived from special domain and describes the semantic information of local GIS. The nature of the Local Ontology Agents can be filtrated to construct a virtual organization what could provides a global scheme. The virtual organization lightens the burdens of guests because they need not search information site by site manually. The application layer what is composed with User Agent, Geo-Agent and Task Execution Agent can apply a corresponding interface to a domain user. The functions that ASISG should provide are:
1) It integrates different spatial information systems on the semantic The Grid management layer establishes a virtual environment that integrates seamlessly all GIS notes.
2) When the resource management system searches data on different spatial information systems, it transfers the meaning of different Local Ontology Agents rather than access data directly. So the ability of search and query can be said to be on the semantic level.
3) The data access procedure is transparent to guests, that is, they could access the information from remote site as current disk because the General Ontology Agent could automatically link data by the Data Agents that link the Ontology concept to GIS data.
4) The capability of processing massive spatial data. Storing, accessing and managing massive spatial data from TB to PB; efficiently analyzing and processing spatial data to produce model, information and knowledge; and providing 3D and multimedia visualization services.
5) The capability of high performance computing and processing on spatial information. Solving spatial problems with high precision, high quality, and on a large scale; and process spatial information in real time or on time, with high-speed and high efficiency.
6) The capability of sharing spatial resources. The distributed heterogeneous spatial information resources are Shared and realizing integrated and inter-operated on semantic level, so as to make best use of spatial information resources,such as computing resources, storage devices, spatial data (integrating from GIS, RS and GPS), spatial applications and services, GIS platforms,
7) The capability of integrating legacy GIS system. A ASISG can not only be used to construct new advanced spatial application systems, but also integrate legacy GIS system, so as to keep extensibility and inheritance and guarantee investment of users.
8) The capability of collaboration. Large-scale spatial information applications and services always involve different departments in different geographic places, so remote and uniform services are needed.
9) The capability of supporting integration of heterogeneous systems. Large-scale spatial information systems are always synthetically applications, so ASISG should provide interoperation and consistency through adopting open and applied technology standards.
10) The capability of adapting dynamic changes. Business requirements, application patterns, management strategies, and IT products always change endlessly for any departments, so ASISG should be self-adaptive.
Two examples are provided in this paper, those examples provide a detailed way on how you design your semantic grid based on Multi-Agent systems and Ontology. In conclusion, the semantic grid of spatial information system could improve the ability of the integration and interoperability of spatial information grid.