Invited Presentation at 1st CIKM 2020 Workshop on Combining Symbolic and Sub-Symbolic Methods and Their Applications (CSSA-CIKM 2020)

Learning With Temporal Knowledge Graphs

MCML PI Volker Tresp, Yunpu Ma and Zhen Han review recently developed learning-based algorithms for temporal knowledge graphs completion and forecasting. Knowledge graphs, also known as episodic or time-dependent knowledge graphs are large-scale event databases that describe temporally evolving multi-relational data.

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02.06.2026

Benjamin Lange: The Real Risk of AI Agents Is Manipulation Through Kindness

MCML Junior Research Group Leader Benjamin Lange examines how trust in AI agents can itself become a source of risk.

02.06.2026

MCML at CVPR 2026

MCML researchers are represented with 25 papers at CVPR 2026 (23 Main, and 2 Workshops).

29.05.2026

MCML at ICRA 2026

MCML researchers are represented with 3 papers at ICRA 2026.

28.05.2026

Zeynep Akata: To Trust AI, We Need to Understand What Goes on Behind the Scenes

MCML PI Zeynep Akata explains that to trust AI, we must understand its inner workings, address foundation model bias, and make explainability central.

27.05.2026

Medical Diagnoses: How AI Explanations Help Doctors

Stefan Feuerriegel shows that AI models can improve diagnostic accuracy in radiology – but how the AI explains its recommendations is crucial.

Invited Presentation at 1st CIKM 2020 Workshop on Combining Symbolic and Sub-Symbolic Methods and Their Applications (CSSA-CIKM 2020)

Learning With Temporal Knowledge Graphs

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