Modelling spatial data in knowledge graphs: a top-down approach for the supply chain monitoring domain

This Thesis analyzes the modelling of spatial and semantic data for the application of supply chain monitoring. For this purpose, the concept of knowledge graphs (KGs) is examined, and scientific findings and techniques of this field are applied. Two basic graph data models are identified from literature: directed edge-labelled and property graph models. Both approaches are considered in detail and it is shown that the models are flexible enough to be transformed into one another. Moreover, it is discussed how "knowledge" is created and may be incorporated in a KG, as well as the crucial role of context. Before considering the incorporation of geographic data into the graph data models presented, some basics from the field of geoinformatics must be given as background. In particular, the geometric-topological as well as semantic characteristics and perspectives are of importance, as these form the potential connecting points to the domains of KGs and supply chain monitoring. The concepts of topology, networks and graphs are contrasted and explained how they work together. Some essential definitions of the supply chain management domain and the consequences for the interaction between supply chain monitoring and geoinformation are outlined. These theoretical considerations are incorporated within the technical study where a semantic model (SCMOn) is developed first by the means of ontology engineering and knowledge from practice from the research project JRC LIVE at the research institution Logistikum Steyr within the software Protégé. Secondly, a spatial KG (sKG-SCMon) is constructed by the use of fictional test data, the conventionally developed ontology, and the software GraphDB Free. The previously defined competency questions form a tool for the verification of the elaborated knowledge graph. Finally, these questions are reformulated into (Geo)SPARQL graph queries, are successfully executed and return the results as predicted by the theory.