Program -Old

Preliminary Schedule

Tue 7 Wed 8 Thu 9 Fri 10 Sat 11
08:30-09:00 Opening
09:00-11:00 Lecture 1 Lecture 3 Lecture 5 Lecture 6 Workshop
11:00-11:20 coffee break
11:20-12:20 Lecture 1 Lecture 3 Lecture 5 Lecture 6 Workshop
12:20-13:30 lunch
13:30-15:30 Lecture 2 Lecture 4 Workshop Workshop Social event
15:30-15:50 coffee break
15:50-17:00 Lecture 2 Lecture 4 Hike to Fløyen Workshop Social event
17:00-19:00 Walk and talk Social dinner

Lecture 1


Aidan Hogan
Knowledge Graphs: Research and Applications

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/).

Lecture 2

Ricardo Guimarães, Ana Ozaki

Reasoning in Knowledge Graphs

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.

Lecture 3

Steven Schockeart, Víctor Gutiérrez-Basulto

Reasoning about Concepts with Ontologies and Vector Space Embeddings

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

Fabian M. Suchanek

Rule Mining and Link Prediction

Coming soon

Lecture 5
Angelika Kimming

Coming soon

Lecture 6


Marija Slavkovic

Automating Moral Reasoning

Coming soon

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 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”?