CS 707: Data and Web Science Seminar (FSS 2021)

The Data and Web Science seminar covers recent topics in data and web science. This term the seminar focuses on Relational Learning.


  • This seminar is organized by Prof. Dr. Rainer Gemulla and Adrian Kochsiek.
  • Available for Master students (2 SWS, 4 ECTS). If you are a Bachelor student and want to take this seminar (2 SWS, 5 ECTS), please contact Prof. Gemulla.
  • Prerequisites: solid background in machine learning
  • Maximum number of participants is 10 students


In this seminar, you will

  • Read, understand, and explore scientific literature
  • Summarize a current research topic in a concise report (10 single-column pages + references)
  • Give two presentations about your topic (3 minutes flash presentation, 15 minutes final presentation)
  • Moderate a scientific discussion about the topic of one of your fellow students
  • Review a (draft of a) report of a fellow student


  • Register as described below.
  • Attend the kickoff meeting on 10 March, 17:15 (tentative).
  • Work individually throughout the semester according to the seminar schedule.
  • Meet your advisor for guidance and feedback.


If you are an MSc student, register via Portal 2 until 1 March. If you are a BSC student, register via email to Adrian Kochsiek until 1 March.

If you are accepted into the seminar, provide at least 4 topics of your preference (your own and/or example topics; see below) by 7 March via email to Adrian Kochsiek. The actual topic assignment takes place soon afterwards; we will notify you via email. Our goal is to assign one of your preferred topics to you.


Each student works on a topic within the area of the seminar along with an accompanying reference paper. Your presentation and report should explore the topic with an emphasis on the reference paper, but not just the reference paper.

We provide example topics and reference papers below. We structured the topics into three blocks: symbolic, neural and neural-symbolic approaches. You can get an overview of relational machine learning approaches on knowledge graphs here and in specific neural approaches here. Each topic is associated with an example reference paper. If you want, you may suggest a different reference paper (let us know after the topic assignment) or a different topic within the relational learning area (talk to us before the topic assignments).

Symbolic Approaches

1. Inductive Logic Programming (BSc students only)
Inductive Logic Programming in a Nutshell
Introduction to Statistical Relational Learning (Ch 3), 2007

2. Markov Logic Networks
Richardson et al.
Markov Logic Networks
Machine Learning, 2006

3. Rule Learning
Galárraga et al.
AMIE: Association Rule Mining under Incomplete Evidence in Ontological Knowledge Bases
WWW, 2013

4. Incremental Rule Learning
Zhou et al.
Mining Rules Incrementally over Large Knowledge Bases
SIAM, 2019


Neural Approaches

5. Knowledge Graph Embeddings with Translational Distance Models
Sun et al.
Rotate: Knowledge Graph Embedding by Relational Rotation in Complex Space
ICLR, 2019

6. Knowledge Graph Embeddings with Matrix Factorization
Trouillon et al.
Complex Embeddings for Simple Link Prediction
ICML, 2016

7. Knowledge Graph Embeddings with Graph Neural Networks
Schlichtkrull et al.
Modeling Relational Data with Graph Convolutional Networks
ESWC, 2018

8. Temporal Knowledge Graph Completion
Goel et al.
Diachronic Embedding for Temporal Knowledge Graph Completion
AAAI, 2020

9. Reasoning with Neural Tensor Networks
Socher et al.
Reasoning with Neural Tensor Networks for Knowledge Base Completion
NIPS, 2013

10. Visual Relations­hip Detection
Chen et al.
Knowledge-Embedded Routing Network for Scene Graph Generation
CVPR, 2019


Neural-Symbolic Approaches

11. Differentiable Inductive Logic Programming
Evans et al.
Learning Explanatory Rules from Noisy Data
Journal of Artificial Intelligence Research, 2018

12. Differentiable Rule Mining
Sadeghian et al.
DRUM: End-to-End Differentiable Rule Mining On Knowledge Graphs
NeurIPS, 2019

13. End to End Differentiable Reasoning
Minervini et al.
Differentiable Reasoning on Large Knowledge Bases and Natural Language
AAAI, 20

14. Joint Rule and Embedding Learning
Ho et al.
Rule Learning from Knowledge Graphs Guided by Embedding Models
ISWC, 2018


Supplementary materials and references