CS 707 Data and Web Science Seminar (HWS 2016)

The Data and Web Science seminar covers recent topics in data and web science. This term, the seminar focuses on statistical relational learning in the context of information extraction and knowledge graphs.


In this seminar, you will
  • Read, understand, and explore scientific literature
  • Summarize a current research topic in a concise report
  • Give two presentations about your topic (one 3 minutes, one 15 minutes)
  • Moderate a scientific discussion about a topic of one of your fellow students
  • Provide feedback to a report and to a presentation of a fellow student



  • Select your preferred topics and register by Sep. 07
  • Attend the kickoff meeting 
  • You will be assigned an advisor, who provides guidance and one-to-one meetings
  • Work individually throughout the semester: explore literature, write a report, peer-review, create and give two presentations
  • Full schedule pdf
  • Kickoff slides


Explore the list of topics below and select at least 3 topics of your preference. Send a ranked list of your selected topics via email to ywang(at)uni-mannheim(dot)de until Sep. 07. We will confirm your registration immediately. The actual topic assignment takes place soon after the registration deadline and we will notify you via e-mail. Our goal is, of course, to assign to you to one of your preferred topics.


The papers and articles listed below serve as an entry point to a topic; you are expected to explore related relevant literature on your own.

Different approaches and the corresponding starting papers are given below:

Matrix Factorization:

Relation Extraction with Matrix Factorization and Universal Schemas

Sebastian Riedel, Limin Yao, Andrew McCallum, Benjamin M. Marlin

Factorization Machines:

CORE: Context-Aware Open Relation Extraction

Fabio Petroni, Luciano Del Corro, Rainer Gemulla


Tensor Factorization:

A Three-Way Model for Collective Learning on Multi-Relational Data

Maximilian Nickel, Volker Tresp, Hans-Peter Kriegel

Rule Mining:

AMIE: Association Rule Mining under Incomplete Evidence in Ontological Knowledge Bases

Luis Galárraga , Christina Teflioudi , Katja Hose, Fabian M. Suchanek


Graph Structure:

Random Walk Inference and Learning in A Large Scale Knowledge Base

Ni Lao, Tom Mitchell, William W. Cohen


Graphical Model:

Knowledge-Based Trust: Estimating the Trustworthiness of Web Sources

Xin Luna Dong, Evgeniy Gabrilovich, Kevin Murphy, Van Dang Wilko Horn, Camillo Lugaresi, Shaohua Sun, Wei Zhang

Pattern Learning:

Open Language Learning for Information Extraction

Mausam, Michael Schmitz, Robert Bart, Stephen Soderland, and Oren Etzioni


Probabilistic Soft Logic:

Knowledge Graph Identification

Jay Pujara , Hui Miao , Lise Getoor , and William Cohen


Students are free to suggest alternative topics.


The following resources may be helpful:

  • Introduction to Statistical Relational Learning,  by Getoor and Taska
  • A Review of Relational Machine Learning for Knowledge Graphs, by Maximilian Nickel, Kevin Murphy, Volker Tresp, Evgeniy Gabrilovich


Giving talks / writing reports:

  • "Giving conference talks", by Prof. Dr. Rainer Gemulla [pdf]
  • "Writing for Computer Science" by Justin Zobel