Knowledge Graph Maintenance

Knowledge graphs are increasingly built using complex multifaceted machine learning-based systems relying on a wide of different data sources. To be effective these must constantly evolve and thus be maintained. Here work is presented on combining knowledge graph construction (e.g. information extraction) and refinement (e.g. link prediction) in end to end systems. The challenges of ongoing knowledge graph maintenance and the role of people in that process are reflected on. Finally, we look to need for a  knowledge scientist in these processes.