Tue 7 Wed 8 Thu 9 Fri 10
08:30-09:00 Welcome and Opening: Inge Jonassen
Chair: Ana Ozaki
09:00-11:00 Lecture 1 Slides Lecture 3 Slides Lecture 5 Slides 1  2  Workshop: Track 2
11:00-11:20 coffee break
11:20-12:20 Lecture 1 Video Lecture 3 Video Lecture 5 Slides 3 Video Workshop: Track 3
12:20-13:30 lunch
13:30-15:30 Lecture 2 Slides Lecture 4 Slides Workshop: Track 1 Lecture 6 Slides
15:30-15:50 coffee break
15:50-17:00 Lecture 2 Video Lecture 4 Video Hike to Fløyen Lecture 6 Video
17:00-19:00 Walk and talk Gather Town Dinner
Sat 11
9:00-10:30 Workshop: Track 4
10:30-10:40 pause
10:40-14:30 Boat Trip & Lunch
14:30- onward Cheers and thank you!


Lecture 1 – Introduction to Knowledge Graphs

Aidan Hogan

Knowledge Graphs have received growing attention in recent years, particularly in scenarios that involve integrating diverse sources of data at large scale. Within such scenarios, Knowledge Graphs have popularised the idea of modelling data following a graph-based abstraction, where nodes represent entities and edges represent the relations between entities. In terms of research, Knowledge Graphs have become a novel point of convergence for different communities, wherein a variety of techniques for creating, enriching, validating and analysing Knowledge Graphs have been proposed, alongside techniques for querying, reasoning, and generating machine learning models over them. In terms of practice, Knowledge Graphs are now used in diverse applications involving question answering, recommendations, classification and prediction, semantic search, information extraction, and more besides. In this lecture, we will provide an introduction to Knowledge Graphs, covering the basics of how they modelled, the techniques that they enable, the research questions that they raise, and the applications in which they have been used.

Aidan Hogan is an Associate Professor at the Department of Computer Science, University of Chile, and an Associate Researcher at the Millennium Institute for Foundational Research on Data (IMFD). His research interests relate primarily to the Semantic Web, Databases and Information Extraction; he has published over one hundred peer-reviewed works on these topics. He has been invited as a lecturer to seven summer schools and he has co-organised three summer schools. He is an author or lead author of three books, the latest of which, entitled “Knowledge Graphs”, is due to be published with Morgan & Claypool; a manuscript of this book is available from arXiv (https://arxiv.org/abs/2003.02320). For further information, see his homepage (http://aidanhogan.com/).

Slides Video

Lecture 2 – Reasoning in Knowledge Graphs


Ricardo Guimarães, Ana Ozaki

Knowledge Graphs (KGs) are becoming increasingly popular in the industry and academia. They can be represented as labelled graphs conveying structured knowledge in a domain of interest, where nodes and edges are enriched with metaknowledge such as time validity, provenance, language, among others. Once the data is structured as a labelled graph one can apply reasoning techniques to extract relevant and insightful information. We provide an overview of deductive, inductive and abductive reasoning approaches for reasoning in KGs.

Ricardo Guimarães is a postdoctoral research fellow at the University of Bergen. He works in Artificial Intelligence (AI), specifically with Knowledge Representation and Reasoning (KR), focusing on Description Logics. Currently, he is working towards the combination of Knowledge Representation approaches with Machine Learning methods, focussing on Ontologies and Knowledge Graphs.

Ana Ozaki is an associate professor at the University of Bergen, Norway. She is an AI researcher in the field of knowledge representation and reasoning and in learning theory. Ozaki is interested in the formalisation of the learning phenomenon so that questions involving learnability, complexity, and reducibility can be systematically investigated and understood. Her research focuses on learning logical theories formulated in description logic and related formalisms for knowledge representation. She is the principal investigator of the project Learning Description Logic Ontologies funded by RCN.

Slides Video

Lecture 3 – Reasoning about Concepts with Ontologies and Vector Space Embeddings

Steven Schockeart, Víctor Gutiérrez-Basulto, Zied Bouraoui

Ontologies and vector space embeddings are among the most popular frameworks for encoding conceptual knowledge. Ontologies excel at capturing the logical dependencies between concepts in a precise and clearly defined way. Vector space embeddings excel at modelling similarity and analogy. Given these complementary strengths, various research lines have focused on developing frameworks that can combine the best of both worlds. In this chapter, we present an overview of the work in this area. We first discuss the theory of conceptual spaces, which was proposed in the 1990s by Gärdenfors as an intermediate representation layer, in between embeddings and symbolic knowledge bases. Second, we discuss approaches where symbolic knowledge is modelled in terms of geometric constraints, which are used to constrain or regularise vector space embeddings. Finally, we discuss methods in which similarity, and other forms of conceptual relatedness, are derived from vector space embeddings and subsequently used to support flexible forms of reasoning with ontologies.

Steven Schockaert is a professor at Cardiff University. His main research interests at the moment are commonsense reasoning, representation learning and lexical semantics. He is editor-in-chief of AI Communications, associate editor of Artificial Intelligence and area editor of Fuzzy Sets and Systems. He also serves on the board of EurAI in the role of treasurer. His work has been supported by a range of funders, including the European Research Council, EPSRC, the Leverhulme Trust and the Research Foundation Flanders. He was the recipient of the 2008 ECCAI Doctoral Dissertation award and the IBM Belgium prize for Computer Science.
Víctor Gutiérrez-Basulto is a lecturer in the School of Computer Science and Informatics at Cardiff University. Before that he hold two prestigious postdoctoral fellowships supported by the European Union and the German Government. His main research interests include foundational aspects of ontology-enriched information systems capturing uncertainty and dynamic aspects of knowledge and more broadly, ontological reasoning under non-standard assumptions. He was PC Chair of RuleML+RR 2020 and Sponsorship Chair of KR 2020.

Lecture 4 – Neuro-Symbolic Methods for Fact Prediction


Armand Boschin, Nitisha Jain, Gurami Kerechashvili and Fabian M. Suchanek

Knowledge bases are typically incomplete. Recent years have seen two approaches to guess missing facts: Rule Mining and Knowledge Graph Embeddings. The first approach is symbolic, and finds rules such as “If two people are married, they most likely live in the same city”. These rules can then be used to predict missing statements. Knowledge Graph Embeddings, on the other hand, are trained to predict missing facts for a knowledge base by mapping entities to a vector space. Each of these approaches has their strengths and weaknesses, and this article provides a survey of works that have taken to combine embeddings and symbolic approaches.

Fabian M. Suchanek is a full professor at the Telecom Paris University in France. Fabian developed inter alia the YAGO knowledge base, one of the largest public general-purpose knowledge bases. This earned him an honorable mention of the SIGMOD dissertation award and the Test of Time Award of The Web Conference (WWW 2018). His interests include information extraction, automated reasoning, and knowledge bases. Fabian has published more than 100 scientific articles, among others at ISWC, VLDB, SIGMOD, WWW, CIKM, ICDE, and SIGIR, and his work has been cited more than 12,000 times.

Lecture 5 — Learning and Reasoning with Graph Data: Neural and Statistical-Relational Approaches

Manfred Jaeger

Graph neural networks (GNNs) have emerged in recent years as a very powerful and popular modeling tool for graph and network data. Though much of the work on GNNs has focused on graphs with a single edge relation, they have also been adapted to multi-relational graphs, including knowledge graphs. In such multi-relational domains, the objectives and possible applications of GNNs become quite similar to what for many years has been investigated and developed in the field of statistical relational learning (SRL). In this lecture I will give a brief overview of the main features of GNN and SRL approaches to learning and reasoning with graph data. I will then in more detail analyze their commonalities and differences  with respect to semantics, representation, parameterization, interpretability, and flexibility. A particular focus will be on relational Bayesian networks (RBNs) as the SRL framework that is most closely related to GNNs. I will show how most common GNN architectures can be directly encoded as RBNs, thus enabling the direct integration of “low level” neural model components with the “high level” symbolic representation and flexible inference capabilities of SRL. This lecture does not require any specific pre-requisites, though a little familiarity with first-order logic for knowledge representation will be an advantage.

Manfred Jaeger studied mathematics in Freiburg, Germany, where he obtained his diploma in mathematics in 1991. He subsequently went to the Max-Planck-Institute for Computer Science in Saarbrücken, where he obtained a PhD in Computer Science from Saarland University in 1995. From 1996-2003 he continued as research associate at the Max-Planck-Institute for Computer Science, and in that period also spent time as postdoctoral researcher at Stanford University, the University of Helsinki, and Freiburg University. In 2002 he obtained the Habilitation in computer science from Saarland University. Since 2003 he is associate professor at the Computer Science department at Aalborg University, Denmark.
Manfred Jaeger has served as associate editor for the Journal of Artificial Intelligence Research and the Artificial Intelligence Journal. He is currently a member of the editorial board of Machine Learning.

Slides 1  Slides 2  Slides 3 Video

Lecture 6 – Automating Moral Reasoning

Marija Slavkovik

Artificial Intelligence ethics is concerned with ensuring a nonnegative ethical impact of ethical impact of researching, developing, deploying and using AI systems. One way to accomplish that is to enable those AI systems to make moral decisions in ethically sensitive situations, i.e., automate moral reasoning. Machine ethics is an interdisciplinary research area that is concerned with the problem of automating moral reasoning. This tutorial presents the problem of making moral decisions and gives a general overview of how a computational (artificial) agent can be constructed to make moral decisions. The tutorial is aimed for students in artificial intelligence who are interested in acquiring a starting understanding of the basic concepts and a gateway to the literature in machine ethics.

Marija Slavkovik is a Professor with the Department of Information Science and Media Studies at the University of Bergen. She researches problems in collective reasoning and decision making. She has been doing research in machine ethics since 2012. Machine ethics studies how moral reasoning can be automated.  Marija has held several seminars, tutorials and graduate courses on AI ethics (http://slavkovik.com/teaching.html). She is interested in the phenomenon of autonomous systems increasingly becoming moral arbitrators by virtue of the dissipation of the machine-society segregation. Automation, particularly of cognition, is not always possible without  automating aspect of ethic reasoning or values. The problem then is whose moral values should have standing, what moral opinions and values should be elicited, how should that be done  and what is the right way to aggregate these “measurements”?

Slides Video

Workshop Program

Track 1: Knowledge Graphs & Embeddings

Oral Presentations:   

  • Aleksandar Pavlovic: Injecting Knowledge Graphs into Machine Learning Models
  • Daniel Daza: Inductive Entity Representations from Text via  Link Prediction
  • Vincenzo Suriani: Semantic Mapping Generalization in Robotics using Knowledge  Embedding Techniques


  • Moritz Blum
  • Taha Halal
  • Victor Lacerda
  • Taraneh Younesian

Track 2: Ontologies & Logic-based Reasoning 

Oral Presentations:

  • Chuangtao Ma: Knowledge Graph Enhanced Schema Matching Network for  Heterogeneous Data Integration
  • Elena Romanenko: Pattern-based Ontology Summarization
  • Filippo De Bortoli: Description Logics with Expressive Cardinality Constraints


  • Anton Gnatenko
  • Arka Ghosh 
  • Nikolaos Kondylidis
  • Cosimo Persia: On the Learnability of Possibilistic Theories –  winner of best poster award by public vote! 🙂

Track 3: Applications 

Oral Presentations:

  • Lauren DeLong: Neurosymbolic Graph Reasoning: the Future of the Biomedical Domain
  • Marina Boudin: Computational approach for drug repositioning: towards an holistic  perspective with knowledge graphs (OREGANO)


  • Adriaan Ludl
  • Aida Ashrafi
  • Aidan ​​Marnane

Track 4: Miscellaneous 

Oral Presentations:

  • Emile van Krieken: Bridging the Discrete-Continuous gap in  Neuro-Symbolic AI – winner of the best oral presentation award by public vote! 🙂
  • Kun Zhang: Question Answering over Heterogeneous Graphs  
  • Nitisha Jain: Knowledge Graph Representation with Embeddings


  • Peter Fratric
  • Sarang Shaik