Benjamin Harbelot

Continuum: a spatiotemporal data model to represent and qualify filiation relationships

This work introduces an ontology-based spatio-temporal data model to represent entities evolving in space and time. A dynamic phenomenon generates a complex relationship network between the entities involved in the process. At the abstract level, the relationships can be identity or topological filiations. The existence of an identity filiation depends on whether the object changes its identity or not. On the other hand, topological filiations are based exclusively on the spatial component, like in the case of growth, reduction, merging or splitting. When combining identity and topological filiations, six filiation relationships are obtained, forming a second abstract level. Upper-level filiation relationships provide better semantic vocabulary to describe the modeled phenomena, thus allowing the implementation of spatial, temporal and identity constraints. In this paper, we present a method based on identity and topological filiation relationships, to improve the capabilities of standard knowledge bases using Semantic Web technologies. Our method enables us to check the consistency of spatio-temporal and semantic data. An example is given in the field of urban growth to show the capabilities of the model.

A Semantic Model to Query Spatial–Temporal Data

There is a growing need for the study of spatial–temporal objects and their relationships. A common approach for this task is the use of relational databases, which unfortunately do not allow inference. In this research, we introduce a new approach that uses the concept of a “continuum” together with ontologies and semantic Web technologies. The continuum allows us to define parent–child relationships between representations of objects. It also allows us to compare the evolution of two different objects and establish the relationships between them along time. Our approach is based on the four-dimensional fluent, which is extended to obtain spatial–temporal qualitative information from the analysis of objects and their relationships. The results of our analysis are later added to our knowledge base, thus enhancing it. Our preliminary results are promising and we plan to further develop the model in the near future.

LC3: A spatio-temporal and semantic model for knowledge discovery from geospatial datasets

There is a need for decision-makers to be provided with both an overview of existing knowledge, and information which is as complete and up-to-date as possible on changes in certain features of the biosphere. Another objective is to bring together all the many attempts which have been made over the years at various levels (international, Community, national and regional) to obtain more information on the environment and the way it is changing. As a result, remote sensing tools monitor large amount of land cover informations enabling study of dynamic processes. However the size of the dataset require new tools to identify pattern and extract knowledge. We propose a model to discover knowledge on parcel data allowing analysis of dynamic geospatial phenomena using time, spatial and thematic data. The model is called Land Cover Change Continuum (LC3) and is able to track the evolution of spatial entities along time. Based on semantic web technologies, the model allows users to specify and to query spatio-temporal informations based on semantic definitions. The semantic of spatial relationships are of interest to qualify filiation relationships. The result of this process permit to identify evolutive patterns as a basis for studying the dynamics of the geospatial environment. To this end, we use CORINE datasets to study changes in a specific part of France. In our approach, we consider entities as having several representations during their lifecycle. Each representation includes identity, spatial and descriptives properties that evolve over time.

A Semantic Web Approach for Geodata Discovery

Currently, vast amounts of geospatial information are offered through OGC’s services. However this information has limited formal semantics. The most common method to search for a dataset consists in matching keywords to metadata elements. By adding semantics to available descriptions we could use modern inference and reasoning mechanisms currently available in the Semantic Web. In this paper we present a novel architecture currently in development in which we use state of the art triplestores as the backend of a CSW service. In our approach, each metadata record is considered an instance of a given class in a domain ontology. Our architecture also adds a spatial dataset of features with toponym values. These additions allow us to provide advance searches based on 1) Instance to class matching, 2) Class to class subsuming relationships, 3) Spatial relationships resulting from comparing the bounding box of a metadata record with our toponym spatial dataset.