The SemGIS project aims at interpreting heterogeneous data by creatingÂ interoperability via a semantic layer between former unrelated spatial and non-spatialÂ data sets. Applications of this semantic layer are to be found in disaster management,Â emergency management and decentralized green energy. In the contextÂ of the german energy transition project, the part of spatial data should not toÂ be underestimated. Data of different kinds are collected constantly in massiveÂ amounts in various data formats. As the process of data format standardizationÂ can be at best a long term goal, information management systems need toÂ be enabled to cope with heterogeneous data directly in order to simplify communicationÂ between existing standards. Crucial problems and lack of efficiencyÂ because of data conversion issues arise in the area of flood management because the compatibility of data sources used by authorities, nongovernmentalÂ organizations and companies is often nonexistent. In an emergency case, thisÂ leads to restrictions in the operational framework of mitigation and thereforeÂ to more risks as well as costs. In the context of energy transition, a commonÂ task is to plan and distribute energy according to local demands and because ofÂ decentralized energy generation also its provision. To quantify those demands,Â solutions for exchanging and interpreting information from different authoritiesÂ and companies are important. The SemGIS projectâ€˜s motivation is therefore toÂ simplify data interoperability using semantic technologies in order to surpass theÂ aforementioned obstacles completely or to a certain extent.
SemGIS goals are:
â€“Â Creating a semantic model which includes Expert knowledge in flood management and emergency management
â€“Â Expert knowledge in extraction and distribution of green energy
â€“Â Data structures and patterns familiar to the cooperation partners current working environment
â€“Â Data interpretation from heterogeneous semantic data and documents for data integration preparation.
â€“Â Development of a mechanism to recognize concepts in heterogeneous data and to annotate its meaning
â€“Â Extension of the system using description logic to enrich the predefined semantic and to increase its usefulness
â€“Â Reasoning in a geospatial context using already existing semantic technologies
â€“Â Application of rule-based analysis to support specialists in decision-making processes
From January to March of this year the project focused on the extraction of ontologies that are defined by frequently occuring formats we have to deal with like INSPIRE, XPlanung Data and AAA-Data and its manual interlinkage into the Semantic Web. Those results and experiences thereof manifested in our publication at ICCSA2016 in Beijing China in July 2016. From April to June algorithms to automatically integrate geodata of unknown schemas have been developed, its shortcomings analyzed and tests have been conducted on various concerned datasets. After an evaluation of the different algorithms and a therefore established base interpretation of data, the research focus from August to October shifted towards the integration of provenance and data quality information into the current knowledge base. By integrating provenance and data quality information into the model we can publish this information to the Semantic Web where such information is rare and we can evaluate the accuracy of the results the SemGIS project will produce in the aspects that are important for the end-user. End of September, a poster presentation of the INSPIRE ontology at the annual INSPIRE conference in Barcelona as well as an aural presentation of the matter took place. From October 2016 we began correspondences with two potential new cooperation partners, SteB KÃ¶ln and the Federal Office for Cartography and Geodesie in Germany. The later of which resulted in a prototype application that highlights quality criteria of OpenStreetMap geometries which are tailormade to the requests of their costumers and their needs (See Figure 2). This prototype will become the basis of one of the PhD thesis being developed in the SemGIS project.
The activities of the year 2016 can be separated into two main parts. The first part concerns in semantic modelling. In the continuity of the previous year research on existing systems to support the disaster management, we have analyzed the different ontologies used in these systems. This analysis aimed at identifying if we can reuse one or several of these existing ontologies in our project according to their goal and their specificity. We have then, designed a processing to integrate automatically and semantically dataset with unknown schema. The second part concerns in using our semantic geographic system for improving the disaster management. More precisely, we have focused on improving action plans which are prepared for the response to a disaster. These plans are assessed after their application to a disaster or after a training of response actors to a disaster situation. However, some tests of the plans can be inapplicable or have a high cost. That is why, we have decided to combine our semantic geographic information system with a simulation system in order to test the response action plans with a lower cost. In order to realize a such system, we have studied the different existing simulation models in order to determine the most adapted to the requirements of a disaster situation management. The most adapted model is a multi-agent system. That is why, new research has been conducted on multi-agent systems and the different agent modellings. In parallel, a study of different response action plans is realized in order to identify accurately the components of disaster response.