Preface

The workshop Knowledge Representation and Representation Learning (KR4L)
was held in conjunction with the 24th European Conference on Artificial Intelli-
gence (ECAI 2020). Its motivation is the currently perceived disconnect between
the areas of Representation Learning (RL) and Knowledge Representation and
Reasoning (KRR). Most of the research is currently concentrated on one area or
the other, yet arguably representation learning is central to make use of knowl-
edge representation and reasoning techniques in modern, scalable AI applica-
tions. This is particularly the case, but not restricted to, the area of Knowledge
Graphs. We welcomed submissions of research contributions between the areas
of RL and KRR.
We want to thank all who contributed to organising this workshop (some, but
not all, are listed below) as well as all who submitted papers.

Organization of KR4L 2020
The programme committee of KR4L 2020 consisted of:
 – Parsa Bagherzadeh, Concordia University, Canada
 – Huajun Chen, University of Zhejiang, China
 – Jiaoyan Chen, University of Oxford, UK
 – Reza Izanloo, Ferdowsi University of Mashhad, Iran
 – Ernesto Jimenez-Ruiz, City University of London, UK
 – Majid Mohammadi, Delft University of Technology, Netherlands
 – Markus Nissl, TU Wien, Austria
 – Guilin Qi, University of Zhejiang, China
 – Evgeny Sherkhonov, University of Oxford, UK
 – Chengjin Xu, University of Bonn, Germany
 – Yadollah Yaghoobzadeh, Microsoft, USA
The senior committee consisted of:
 – Georg Gottlob, University of Oxford, UK and TU Wien, Austria
 – Jens Lehmann, Fraunhofer IAIS and University of Bonn, Germany
The organization committee and PC chairs have been:
 – Mojtaba Nayyeri, University of Bonn, Germany
 – Emanuel Sallinger, TU Wien, Austria, and University of Oxford, UK
 – Sahar Vahdati, InfAI, Germany, and University of Oxford, UK
The publicity chair has been:
 – Lianlong Wu, University of Oxford, UK

The editors