The SemGIS project aims at interpreting heterogeneous data by creating interoperability via a semantic layer between former unrelated spatial and non-spatial data sets....
The presented project is an internship project by two french masterâ€™s degree students. It has been realized in partnership with the German railway company, Deutsche Bahn. The...
This position paper highlights current problems linked to the aspects of the multi-agency collaboration during disaster response. The coordination and cooperation depend on the information sharing and use which must face up to interoperability, access rights, and quality problems. The research project aims at providing an assessment of information impact on the disaster response in order to support the decisionmaking about what information shared or what quality of data used to improve the response efficiency. Our research approach propose to combine an information system able to integrate heterogeneous data and a simulation system to assess different strategies of information sharing, dissemination and use. A knowledge base is used as a bridge between information system and simulation system. This knowledge base allows for designing dynamically a simulation according to open data and for managing the own knowledge and information known by each agent.
For several years, many researchers tried to semantically integrate geospatial datasets into the semantic web. Although, there are many general means of integrating interconnected relational datasets (e.g. R2RML), importing schema-less relational geospatial data remains a major challenge in the semantic web community. In our project SemGIS we face significant importation challenges of schema-less geodatasets, in various data formats without relations to the semantic web. We therefore developed an automatic process of semantification for aforementioned data using among others the geometry of spatial objects. We combine Natural Language processing with geographic and semantic tools in order to extract semantic information of spatial data into a local ontology linked to existing semantic web resources. For our experiments, we used LinkedGeoData and Geonames ontologies to link semantic spatial information and compared links with DBpedia and Wikidata for other types of information. The aim of our experiments presented in this paper, is to examine the feasibility and limits of an automated integration of spatial data into a semantic knowledge base and to assess its correctness according to different open datasets. Other ways to link these open datasets have been applied and we used the different results for evaluating our automatic approach.
With the climate change, disasters occur more frequently and the need for efficient disaster management systems becomes highly recommended to save lives. This paper deals with a study of existing systems, with the intention of determining the main recent improvement in the domain. The heterogeneous data integration process is a major central point. Thus, a semantic system with three main components is proposed as a new position in the disaster management systems. These three components are a knowledge base, a reasoner and a semantic catalogue. The knowledge base provides a controlled vocabulary and allows storing information retrieval. The semantic catalogue facilitates the access to data sources adapted to user's and agent's needs. The reasoner analyzes the information in the knowledge base thus replying to the user queries. In addition, the reasoner aims at adding automatically new data sources in the semantic catalogue. The fast access to a great number of data sources is of benefit for decision-making systems such as disaster management systems.
The paper presents work on implementation of semantic technologies within a geospatial environment to provide a common base for further semantic interpretation. The work adds on the current works in similar areas where priorities are more on spatial data integration. We assert that having a common unified semantic view on heterogeneous datasets provides a dimension that allows us to extend beyond conventional concepts of searchability, reusability, composability and interoperability of digital geospatial data. It provides contextual understanding on geodata that will enhance effective interpretations through possible reasoning capabilities. Â We highlight this through use cases in disaster management and planned land use that are significantly different. This paper illustrates the work that firstly follows existing Semantic Web standards when dealing with vector geodata and secondly extends current standards when dealing with raster geodata and more advanced geospatial operations.