Abstract. This paper describes categories for landscape elements in the language of the Yindjibarndi people, a community of Indigenous Australians. Yindjibarndi terms for topographic features were obtained from dictionaries, and augmented and refined through discussions with local language experts in the Yindjibarndi community. In this paper, the Yindjibarndi terms for convex landforms and for water bodies are compared to English-language terms used to describe the Australian landscape, both in general terms and in the AUSLIG Gazetteer. The investigation found fundamental differences between the two conceptual systems at the basic level, supporting the notion that people from different places and cultures may use different categories for geographic features.

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. Many geographic terms, such as “river ” and “lake”, are vague, with no clear boundaries of application. In particular, the spatial extent of such features is often vaguely carved out of a continuously varying observable domain. We present a means of defining vague terms using standpoint semantics, a refinement of the philosophical idea of supervaluation semantics. Such definitions can be grounded in actual data by geometric analysis and segmentation of the data set. The issues raised by this process with regard to the nature of boundaries and domains of logical quantification are discussed. We describe a prototype implementation of a system capable of segmenting attributed polygon data into geographically significant regions and evaluating queries involving vague geographic feature terms.

The human body is a system made of systems. The body is divided into bodily systems proper, such as the endocrine and circulatory systems, which are subdivided into many sub-systems at a variety of levels, whereby all systems and subsystems engage in massive causal interaction with each other and with their surrounding environments. Here we offer an explicit definition of bodily system and provide a framework for understanding their causal interactions. Medical sciences provide at best informal accounts of basic notions such as system, process, and function, and while such informality is acceptable in documentation created for human beings, it falls short of what is needed for computer representations. In our analysis we will accordingly provide the framework for a formal definition of bodily system and of associated notions.

For extracting the characteristics a specific geographic entity, and notably a place, we propose to use dynamic Extreme Tagging Systems in combination with the classic approach of static KR models like ontologies, thesauri and gazetteers. Indeed, we argue that in local search, the what that is queried is implicitly about places. However existing knowledge representation (KR) models, such as ontologies based on logical theories, conceptual spaces, affordance or other, cannot capture in isolation all aspects of the meaning of a place. Therefore we propose to use a combination of them based on the underlying notion of differences, linked elements of meaning without commitment to any KR model. Mapping to elements of different KR models can be made later to follow the requirements of a given task, supported by a KR representation of the elements that support this task. We show the usefulness of the approach for local search by applying it to the notion of place defined as a location that supports a homogeneous affordance field, i.e. the spatial area which allows me the do a particular thing, while allowing the homogeneity of movement, meaning that the previous field is not interrupted by any boundaries.

Abstract. Higher level semantics are considered useful in the geospatial domain, yet there is no general consensus on the form these semantics should take. Indeed, knowledge representation paradigms such as classification based ontologies do not always pay tribute to the complexity of geospatial semantics. Other approaches, originating from psychology, linguistics, philosophy or cognitive sciences are regularly investigated to enrich the GIScientist’s representational toolbox. However, each of these techniques is often used to the exclusion of others, creating new representational difficulties, or merely as a useful addendum to host theories with which they only superficially integrate. The present work is an attempt to introduce a common ground to these techniques by reducing them to the notion of differences or difference spaces. Differences are discernible properties of the environment, detected or produced by a computational process. I describe the following semantic frameworks: category-based ontologies, conceptual spaces, affordance based models, image schemata, and multi representation, explaining how each of them can be projected to a model based on differences. Illustrative examples from table top and geographic space are produced in order to show the model in use. 1

Abstract. The categorization of our environment into feature types is an essential prerequisite for cartography, geographic information retrieval, routing applications, spatial decision support systems, and data sharing in general. However, there is no a priori conceptualization of the world and the creation of features and types is an act of cognition. Humans conceptualize their environment based on multiple criteria such as their cultural background, knowledge, motivation, and particularly by space and time. Sharing and making these conceptualizations explicit in a formal, unambiguous way is at the core of semantic interoperability. One way to cope with semantic heterogeneities is by standardization, i.e., by agreeing on a shared conceptualization. This bears the danger of losing local diversity. In contrast, this work proposes the use of microtheories for Spatial Data Infrastructures, such as INSPIRE, to account for the diversity of local conceptualizations while maintaining their semantic interoperability at a global level. We introduce a novel methodology to structure ontologies by spatial and temporal aspects, in our case administrative boundaries, which reflect variations in feature conceptualization. A local, bottom-up approach, based on non-standard inference, is used to compute global feature definitions which are neither too broad nor too specific. Using different conceptualizations of rivers and other geographic feature types, we demonstrate how the present approach can improve the INSPIRE data model and ease its adoption by European member states. 1

Abstract: Due to popular use of internet and fast progress of communications technology, the researches related to GIS in environmental models is being focused to use Geospatial Information System (GIS) in a Service Oriented Architecture (SOA). In this architecture, the geo service requesters can access the provided geo-services through the communication network by using client/server method. Effectiveness of the method depends on the existence of interoperability especially at semantic level. Lack of semantic interoperability makes obstacles for automated discovery of geo-services. The current article proposes a methodology based on ontology for discovering field-based geo-services. An ontological structure including the ontology of measurement theory, the core ontology of geo services and the upper ontology supports semantic framework for this methodology. In the other words, these ontologies provide required knowledge for describing ontologies of provided and requested geo-services. Sample ontologies have been built to test the implementation of the proposed methodology for discovering geo-services. In this regard, a geo-service discovery application has been developed by this research in order to implement a prototype of the methodology. Key words: Interoperability • geo-service • ontology • description logic • OWL language

When people think spatially, they do not usually consider geographic coordinates nor projections. Instead, they think about things that serve as spatial references and the relations between such things. Facing the questions that have a spatial sense, people do not answer with maps or coordinates, but use some references whose spatial location is "well known". For instance, the answer of a conventional geographic information system to the question "Where is the CIC? " would be "in coordinates 19.50314°N, 99.14759°W". In contrast, a person would answer "in Zacatenco " or "near Eje Central". The semantic processing attempts to enrich an abstraction level similar to the one that people use commonly. This processing, applied to spatial data, does not depend on scales, resolutions, projections or others that are fundamental in conventional systems. We assume that the first step for making semantic processing is the semantic description of "raw " spatial data. Such description is the identification of the objects contained in data and the location of such objects within a conceptual framework, where they get a meaning. In this work, we present a methodology for making this semantic description using as a case study the digital elevation models. The methodology is build up of three stages: conceptualization, to define the conceptual framework of the description; synthesis, to process "raw " spatial data and to obtain the spatial objects

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