The new TRANSRAZ project is based on the research results of the previous project TOPORAZ - "Nürnberger Topographie in Raum und Zeit" (Leibniz contest, 2015-2018). In the previous project the virtual research environment (VRE) TOPORAZ was developed. For the first time, a scientifically based 3D model with several time layers of Nuremberg's central square was created to illustrate the historical development of this urban space (from the Middle Ages to the present day). In an innovative way, TOPORAZ combines an interactive 3D city model with historical sources (texts, images) that are queried directly via a networked database.
The new TRANSRAZ project transfers the innovative VRE into the application. To this end, the research area must be extended from a city square to the entire urban space of the historical center with approx. 3,000 houses. Only in this way can Nuremberg's social networks be fully explored. In addition, all TRANSRAZ data are published as Linked Open Data (LOD) to ensure that the FAIR principles are substantially supported.
To this end, in contrast to the previous project, Big Data methods must be used for (semi-)automatic indexing, which gives the project an additional focus on analysis methods with artificial intelligence. Our Information Service Engineering unit is responsible for enriching the data with external information resources related to topographies, persons, organizations, places, and events. For this purpose a project-specific ontology is developed on which the TRANSRAZ knowledge graph is based. The data used are generated form project-relevant historical sources by means of sematic analysis methods such as Natural Language Processing.
The e-Research unit is essentially responsible for the further enhancement of the TOPORAZ VRE with its database. At the same time, it pursues the transfer goals through cooperation with post-use institutions and the conception and implementation of offers for Citizen Science. This enables and promotes the transfer of scientific findings to the general public.
O. Bruns, T. Tietz, M. B. Chaabane, M. Portz, F. Xiong, and H. Sack:The Nuremberg Address Knowledge Graph. In: Proceedings of the 17th Extended Semantic Web Conference, P&D Track (ESWC 2021). (To be published) [local copy (PDF), Video, Website].