no code implementations • 15 May 2023 • Yushan Liu, Bailan He, Marcel Hildebrandt, Maximilian Buchner, Daniela Inzko, Roger Wernert, Emanuel Weigel, Dagmar Beyer, Martin Berbalk, Volker Tresp
Global crises and regulatory developments require increased supply chain transparency and resilience.
1 code implementation • 4 Aug 2022 • Dominik Dold, Josep Soler Garrido, Victor Caceres Chian, Marcel Hildebrandt, Thomas Runkler
Knowledge graphs are an expressive and widely used data structure due to their ability to integrate data from different domains in a sensible and machine-readable way.
1 code implementation • 3 Jun 2022 • Tong Liu, Yushan Liu, Marcel Hildebrandt, Mitchell Joblin, Hang Li, Volker Tresp
We investigate the calibration of graph neural networks for node classification, study the effect of existing post-processing calibration methods, and analyze the influence of model capacity, graph density, and a new loss function on calibration.
1 code implementation • 15 Dec 2021 • Yushan Liu, Yunpu Ma, Marcel Hildebrandt, Mitchell Joblin, Volker Tresp
Conventional static knowledge graphs model entities in relational data as nodes, connected by edges of specific relation types.
1 code implementation • 21 Sep 2021 • Victor Caceres Chian, Marcel Hildebrandt, Thomas Runkler, Dominik Dold
Over the recent years, a multitude of different graph neural network architectures demonstrated ground-breaking performances in many learning tasks.
no code implementations • 8 Sep 2021 • Martin Ringsquandl, Houssem Sellami, Marcel Hildebrandt, Dagmar Beyer, Sylwia Henselmeyer, Sebastian Weber, Mitchell Joblin
The application of graph neural networks (GNNs) to the domain of electrical power grids has high potential impact on smart grid monitoring.
1 code implementation • 13 Jul 2021 • Rajat Koner, Hang Li, Marcel Hildebrandt, Deepan Das, Volker Tresp, Stephan Günnemann
We conduct an experimental study on the challenging dataset GQA, based on both manually curated and automatically generated scene graphs.
1 code implementation • 18 Mar 2021 • Yushan Liu, Marcel Hildebrandt, Mitchell Joblin, Martin Ringsquandl, Rime Raissouni, Volker Tresp
Biomedical knowledge graphs permit an integrative computational approach to reasoning about biological systems.
no code implementations • 10 Jul 2020 • Yushan Liu, Marcel Hildebrandt, Mitchell Joblin, Martin Ringsquandl, Volker Tresp
The graph structure of biomedical data differs from those in typical knowledge graph benchmark tasks.
no code implementations • 2 Jul 2020 • Marcel Hildebrandt, Hang Li, Rajat Koner, Volker Tresp, Stephan Günnemann
We propose a novel method that approaches the task by performing context-driven, sequential reasoning based on the objects and their semantic and spatial relationships present in the scene.
no code implementations • 9 Jan 2020 • Marcel Hildebrandt, Jorge Andres Quintero Serna, Yunpu Ma, Martin Ringsquandl, Mitchell Joblin, Volker Tresp
The underlying idea is to frame the task of triple classification as a debate game between two reinforcement learning agents which extract arguments -- paths in the knowledge graph -- with the goal to justify the fact being true (thesis) or the fact being false (antithesis), respectively.
2 code implementations • 2 Jan 2020 • Marcel Hildebrandt, Jorge Andres Quintero Serna, Yunpu Ma, Martin Ringsquandl, Mitchell Joblin, Volker Tresp
The main idea is to frame the task of triple classification as a debate game between two reinforcement learning agents which extract arguments -- paths in the knowledge graph -- with the goal to promote the fact being true (thesis) or the fact being false (antithesis), respectively.