Prof. Dr. Felix Naumann

Analytics Over Dynamic Knowledge Graphs

Dr.-Ing. Alexander Albrecht (bakdata), Ramin Gharib (bakdata)


Knowledge graphs (KGs) have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data.*

In KGs nodes correspond to entities, and edges correspond to relations between two connected entities. An edge in a KG represents a fact stored in the form of “<subject><predicate><object>”, (e.g., “<Robert Pattinson><starred-in><Tenet>”).

Given these facts from data sources, we focus on analytics over dynamic KGs in this seminar. For example, implementing methods figuring out who belongs to which community in Hollywood.

In this seminar, KG analytics may comprise:

  • Identify important (“central”) nodes or edges
  • Connectivity analysis
  • Structural characteristics, such as subgraph pattern
  • Community detection
  • Rule mining
  • etc.

Students will choose a dataset/use-case and implement efficient analytics over dynamic KGs using current research methods and approaches.**

* Hogan, Aidan, et al. "Knowledge graphs." ACM Computing Surveys (CSUR) 54.4 (2021): 1-37. https://dl.acm.org/doi/pdf/10.1145/3447772

** Besta, Maciej, et al. "Practice of streaming processing of dynamic graphs: Concepts, models, and systems." IEEE Transactions on Parallel and Distributed Systems (2021). https://arxiv.org/pdf/1912.12740.pdf


For this seminar, participants require the following prerequisites

Time Table

Slot: Thursdays, 09:15 – 10:45 in F-E.06 

20.10.Introduction - open for everybody interested
01.11. Announcement of project teams
03.11.First bi-weekly meeting
17.11.Bi-weekly meeting
24.11.First Presentations: Use-Case & Algorithm (Paper)
08.12.Bi-weekly meeting 
05.01.Bi-weekly meeting 
12.01.Intermediate Presentations: Implementation Approach
26.01.Bi-weekly meeting
09.02.Bi-weekly meeting
tbaFinal Presentations
31.03.Code & documentation (on GitHub)



  • Project seminar for master students
  • 6 credit points, 4 SWS
  • Weekly meetings: either as group meetings or individual team meetings with a supervisor
  • Supervisor: Dr.-Ing. Alexander Albrecht, M.Sc. Ramin Gharib, assisted by Tobias Bleifuß
  • The first date serves as an introduction to the topic and the seminar.


In teams of two students, the students will complete the following tasks (percentages for grading):

(10%) Active participation during all seminar events.

(20%) Intermediate presentation demonstrating insights regarding your research project.

(00%) Regular meetings with advisor.

(20%) Implementation.

(20%) Final presentation demonstrating your implementation.

(30%) Code & documentation (on GitHub). The documentation should contain information on how to execute and evaluate your solution. Furthermore, it should also show strengths and weaknesses of the implementation.