Knowledge Graphs (KGs) in recent years have become a melting pot of Arti cial Intelligence (AI) technologies. Two at rst glance very di erent types of AI play a particularly important role here: Knowledge Representation and Reasoning (KRR) and Representation Learning (RL). The area of KRR is often associated with traditional AI techniques, while RL is associated with what is typically summarized as machine learning (ML). Yet, while in Knowledge Graphs both types of technologies come together, there currently is a perceived disconnect between the areas of RL and KRR. Most of the research is currently concentrated on one area or the other, yet arguably representation learning is central to making use of knowledge representation and reasoning techniques in modern, scalable AI applications. This plenary talk opens the International Workshop on Knowledge Representations and Representation Learning (KR4L) 2020, part of the European Conference on Arti cial Intelligence (ECAI) 2020. In this plenary talk, we give an overview of the erea of Knowledge Graphs with a particular focus on KR and RL. In particular it is divided into four parts: Modern Knowledge Graphs { A motivation for the use of modern Knowledge Graphs. { Financial Knowledge Graphs as a particularly interesting area.
Perspectives and Future { We are going to consider multiple perspectives on Knowledge Graphs, including the layered perspective that looks at KGs as representation tools, management systems and application services.
{ The future of KRR and RL in KGs.
We also bridge the six accepted regular papers, one invited paper and two panels of the International Workshop on Knowledge Representations and Representation Learning (KR4L) 2020, spanning many aspects of KRR and RL. This includes the use of RL and ML techniques for KRR: { Traversing Knowledge Graphs with Good Old (and New) Joins { An Evolutionary Algorithm for Rule Learning over Knowledge Graphs The use of KRR in RL and ML: { Cluster Discovery from Sensor Data Incorporating Expert Knowledge { The E ect of Rule Injection in Leakage Free Datasets { A Performance Strategy: Multiple Slices of a KGE Model in Low Dimensions And nally those where KRR and RL/ML techniques are used jointly: { Weaving Enterprise Knowledge Graphs:
The Case of Company Ownership Graphs (Invited Paper) { Blockchains as Knowledge Graphs { Blockchains for Knowledge Graphs (Vision Paper) We also give an overview of the two panels: { Future Directions - Looking Ahead
Lead by Sahar Vahdati and Mojtaba Nayyeri (InfAI and University of Bonn)