Interoperability is a crucial problem for geographic information systems. The transfer of data and models between different systems requires the ability to set up a correspondence between concepts in one system to concepts in the other. Concept matching is helped by ontologies. However, the challenge of making ontologies themselves interoperable continues. In other words, given two geo-ontologies, the basic question is: to which degree are these two geo-ontologies interoperable? In this paper, we consider that a geo-ontology describes things that can be assigned to locations on the surface of the Earth and relations between these things. A geo-ontology has concepts that correspond to physical and social phenomena in the real world. We suggest a classification of these concepts based on their use for describing geo-objects. We present a basic set of concepts for a geographical ontology, based on descriptions of the physical world and of the social reality. We also present a framework for measuring the degree of interoperability between geo-ontologies. We consider that this problem is a special case of Bernstein’s model management algebra for metadata descriptions. We propose to use a matching operator for measuring

Abstract. Due to ever larger ORM models and ORM-represented ontologies, information management and its GUI representation is even more important. One useful mechanism is abstraction, which has received some attention in conceptual modelling and implementation, as well as its foundational characteristics. Extant heuristics for ORM abstractions are examined and enriched with several foundational aspects of abstraction. These improvements are applicable to a wider range of types of representations, including conceptual models and ontologies, thereby not only alleviating the Database Comprehension Problem, but also facilitate conceptual model and ontology browsing. 1

integration of vector-based, large-scale topographic data, within the scope of the WalkOnWeb project. This research focuses on solving semantic heterogeneity of topographic feature classes. The use of new semantic web technologies, such as the Web Ontology Language, is investigated and demonstrated in a practical example of road networks in topographic databases. The proposed methodology is based on a Formal Concept Analysis to extract classifying properties from topographic feature classes. In the next step ontologies are developed describing the topographic feature classes and their classifying properties in terms of an upper ontology. The ontologies enable automatic reasoning about topographic feature classes based on their semantic meaning. The ability to derive super/sub class relationships and equivalences between topographic feature classes is an important development in order to achieve semantic interoperability.

With the increasing access to various types of remotely sensed and ancillary spatial data, there is a growing demand for an independent source of reliable ground data to systematically support information extraction for gaining an understanding of earth systems. Conventionally maps, ad-hoc sample campaigns or exclusively remotely sensed data are used but these approaches are not well suited for effective data integration. This paper discusses avenues towards alternative methods for field data acquisition geared to the extraction of information from remotely sensed and ancillary spatial datasets from a geoscience perspective. We foresee that the merging of expertise in recently developed mobile technology, database design and thematic geoscientific knowledge, provides the potential to deliver innovative strategies for the acquisition and analysis of ground data that could profoundly alter the methods how we model earth systems today. With further development and maturation of such research methodologies we ultimately envisage a situation where planners and researchers can with the same ease as they nowadays download remotely sensed data over the web, download standardized and reliable ‘factual ’ datasets from the surface of the earth to process downloaded remotely sensed data for effective extraction of information. This could result in a worldwide reduction of duplicative ground surveys and at the same time provide a wealth of information that is far more relevant to the societal needs, as the deliveries of today. 1.

Abstract. An ontology-based generalization scheme is presented with specific reference to object attribute data subjected to space and time constrained extraction and presentation. The method is expected to be of value in mobile GIS for providing travelers with additional spatial information while moving. The space constraint is given by the boundary of an object-specific area of information relevance. The time constraint is given by the number of time units available to present the information as a function of the speed of travel and the spatial distribution of objects. An algorithm for changing the duration of the attribute data presentation is presented. A geodata-ontology is used to specify meaningful transitions betweeen levels of generalization. 1

Abstract. Existing approaches for the processing of location-dependent queries implicitly assume location data expressed at maximum precision (e.g., GPS). However, there exist applications where managing location data with this precision is not required and may even be inconvenient. Thus, for example, a user could just be interested in the city that a train is currently traversing. In this situation, retrieving the precise geographic coordinates would lead to an unnecessary overhead in terms of the data communications needed to track the continuously changing current location. Moreover, the user would need some mechanism to translate the coordinates into the corresponding city. In this paper, we stress the importance of a query processing approach that adapts itself to the needs of the user and the level of resolution required: the user should be able to express queries and retrieve results according to his/her own terminology for locations (GPS, cities, states, provinces, or any other geographical area). We have implemented a prototype to test the functionality and the interest of location granules and show the independence between the query processing approach and different ways of presenting the answers. 1

Abstract. In this paper we describe a framework for the application of semantic granularities to the Semantic Web. Given a data source and an ontology formalizing qualities which describe the source, we define a dynamic granularity system for the navigation of the repository according to different levels of detail, i.e., granularities. Semantic granularities summarize the degree of informativeness of the qualities, taking into account both the individuals populating the repository, which concur in the definition of the implicit semantics, and the ontology schema, which gives the formal semantics. The method adapts and extends to ontologies existing natural language processing techniques for topics generalization. 1.

This chapter is concerned with multidimensional data models for spatial data warehouses. Over the last few years different approaches have been proposed in the literature for modelling multidimensional data with geometric extent. Nevertheless, the definition of a comprehensive and formal data model is still a major research issue. The main contributions of the chapter are twofold: First, it draws a picture of the research area; second it introduces a novel spatial multidimensional data model for spatial objects with geometry (MuSD – multigranular spatial data warehouse). MuSD complies with current standards for spatial data modelling, augmented by data warehousing concepts such as spatial fact, spatial dimension and spatial measure. The novelty of the model is the representation of spatial measures at multiple levels of geometric granularity. Besides the representation concepts, the model includes a set of OLAP operators supporting the navigation across dimension and measure levels. Copyright © 2006, Idea Group Inc. Copying or distributing in print or electronic forms without written

With the increasing access to various types of remotely sensed and ancillary spatial data, there is a growing demand for an independent source of reliable ground data to systematically support information extraction for gaining an understanding of earth systems. Conventionally maps, ad-hoc sample campaigns or exclusively remotely sensed data are used but these approaches are not well suited for effective data integration. This paper discusses avenues towards alternative methods for field data acquisition geared to the extraction of information from remotely sensed and ancillary spatial datasets from a geoscience perspective. We foresee that the merging of expertise in recently developed mobile technology, database design and thematic geoscientific knowledge, provides the potential to deliver innovative strategies for the acquisition and analysis of ground data that could profoundly alter the methods how we model earth systems today. With further development and maturation of such research methodologies we ultimately envisage a situation where planners and researchers can with the same ease as they nowadays download remotely sensed data over the web, download standardized and reliable `factual' datasets from the surface of the earth to process downloaded remotely sensed data for effective extraction of information. This could result in a worldwide reduction of duplicative ground surveys and at the same time provide a wealth of information that is far more relevant to the societal needs, as the deliveries of today.

Un algoritmo general para la descomposición semántica de geo-imágenes