We research the automated generation of knowledge graphs and the integration of knowledge from heterogenous sources. Knowledge graphs of structured data and knowledge can be semantically fused and are often the reason why Artificial intelligence (AI) applications can be so easily explained and their results so easily understood.
Increasing digitalization means the quantity of data available worldwide doubles in size every two years – and with it grows its diversity. This explains the increasing trend for using knowledge graphs integrating many different data sources. This technology is the basis for many AI applications and AI assistants that would otherwise not work without them: Knowledge graphs are behind solutions used to retrieve information but also behind question answering systems such as those employed in chatbots.
We rely on uniform vocabulary and standard data format conventions, particularly from linked open data (W3C and Resource Description Framework, RDF) in order to generate automated global knowledge graphs. We worked with Bonn university to develop open source techniques to generate large knowledge graphs and retrieve information from them. We also contribute to many open knowledge graphs such as the DBpedia knowledge graph extracted from Wikipedia. Many of these are freely available.
Scalable Semantic Analytics Stack (SANSA Stack)
In cooperation with Bonn university and other research partners we have developed a library to help us process large knowledge graphs. The application is based on hybrid AI techniques and combines Machine Learning processes with semantic analysis methods. Large quantities of data can be processed in a highly scalable fashion and analyzed thanks to the open source software.