KR4L 2020
Knowledge Representation and Representation Learning 2020

Table of Contents

• Preface
  
  Summary: There were 10 papers submitted for peer-review to this workshop. Out of these, 8 papers were accepted for this volume, 6 as regular papers and 2 as invited short papers.

Session 1: RL for Knowledge Representation and Reasoning (Knowledge Representation and Reasoning)

• Knowledge Graphs: Meeting Point of Knowledge Representation and Representation Learning 1-2 (2)
  Emanuel Sallinger
• Traversing Knowledge Graphs with Good Old (and New) Joins 3-9 (7)
  Paolo Atzeni , Luigi Bellomarini , Davide Benedetto , Emanuel Sallinger
• Cluster Discovery from Sensor Data Incorporating Expert Knowledge 10-21 (12)
  Szymon Bobek , Agnieszka Trzcinkowska , Edyta Brzychczy , Grzegorz J. Nalepa

Session 2: KRR for Representation Learning (Representation Learning with Knowledge Representation)

• Augmenting Logic-based Knowledge Graphs: The Case of Company Graphs 22-27 (6)
  Paolo Atzeni , Luigi Bellomarini , Michela Iezzi , Emanuel Sallinger , Adriano Vlad
• The Effect of Rule Injectionin a Leakage Free Dataset 28-35 (8)
  Mirza Mohtashim Alam , Mojtaba Nayyeri , Chengjin Xu , Md Rashad Al Hasan Rony , Hamed Shariat Yazdi , Afshin Sadeghi , Jens Lehmann
• Multiple Run Ensemble Learning with Low Dimensional Knowledge Graph Embeddings 36-42 (7)
  Chengjin Xu , Mojtaba Nayyeri , Sahar Vahdati , Jens Lehmann

Session 3: KRR and RL Joint Applications (Knowledge Graphs for Cognitive Science)

• Blockchains as Knowledge Graphs - Blockchains for Knowledge Graphs (Vision Paper) 43-51 (9)
  Luigi Bellomarini , Markus Nissl , Emanuel Sallinger
• An Evolutionary Algorithm for Rule Learning over Knowledge Graphs 52-59 (8)
  Lianlong Wu , Emanuel Sallinger , Evgeny Sherkhonov , Sahar Vahdati , Georg Gottlob