DWS Area: Data Science

Four papers of the DWS group got accepted at the 22nd Extended Semantic Web Conference. This year, the acceptance rate was 22.4% for full papers in the research track.

On Thursday, September 26th, Michael Schlechtinger has defended his PhD thesis on “Investigating, Predicting, and Mitigating Collusive Behavior in Deep Reinforcement Learning-Based Pricing AIs”, supervised by Prof. Heiko Paulheim. The thesis has been conducted in the KarekoKI project, an ...

Dr. Rita Sousa has achieved an Open Science Grant for providing up-to-date Knowledge Graph Embeddings

Sven Hertling has defended his PhD thesis on “Knowledge Graph Generation and Integration at Scale”

How can Artificial Intelligence (AI) be used to make comprehensible decisions? This is the subject of a new project at the DWS group, which has received funding from the Federal Ministry of Education and Research (BMBF). Knowledge graphs influence our lives on a daily basis and yet they are little ...

Nicolas Heist has successfully defended his thesis on “Exploiting Semi-Structured Information in Wikipedia for Knowledge Graph Construction”

We are happy to announce that four PhD Students from the AI group have their papers accepted for IJCAI 2024 (A* ranked) in Jeju, Korea: Julia Gastinger: “History repeats itself: A Baseline for Temporal Knowledge Graph Forecasting” (Research Track) Andrej Tschalzev: “Enabling Mixed Effects Neural ...

Jan Portisch, a former PhD student at the Data and Web Science Group and now a Lead Software Architect at SAP Signavio, has received the SWSA Distinguished Dissertation Award.

The BMBF funds a new project in the field of medical AI and systems medicine. The goal of the KI-DiabetesDetektion project is to integrate indication data from various sources in a knowledge graph and apply machine learning methods to improve the early stage detection of Diabetes.

The book “Embedding Knowledge Graphs with RDF2vec”, co-authored by Heiko Paulheim together with DWS alumni Petar Ristoski and Jan Portisch, has been published at Springer. The book explains the ideas behind one of the most well-known methods for knowledge graph embedding of transformations to ...