2026 Volker Tresp: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	63	2731	2649	119	116	355	17989

Overview

Volker Tresp is affiliated with Ludwig-Maximilians-Universität München in Germany. Their research predominantly falls within the field of Computer Science, with a significant focus on Artificial Intelligence. The breadth of their work also covers subfields including Computer Vision and Pattern Recognition, Molecular Biology, Management Science and Operations Research, and Information Systems.

The scientist's research interests span multiple advanced topics, which include:

Topic Modeling
Advanced Graph Neural Networks
Multimodal Machine Learning Applications
Data Quality and Management
Natural Language Processing Techniques
Advanced Image and Video Retrieval Techniques
Domain Adaptation and Few-Shot Learning

Volker Tresp has contributed extensively to scholarly literature, frequently publishing in several academic venues. The most common publication outlets for their work are:

arXiv (Cornell University)
Proceedings of the AAAI Conference on Artificial Intelligence
Zenodo (CERN European Organization for Nuclear Research)
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Lecture notes in computer science

Several recent papers highlight the range of their research focus. Notable works include:

TLogic: Temporal Logical Rules for Explainable Link Forecasting on Temporal Knowledge Graphs (2022), published in Proceedings of the AAAI Conference on Artificial Intelligence
Learning Neural Ordinary Equations for Forecasting Future Links on Temporal Knowledge Graphs (2021), published in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings (2020), published on arXiv (Cornell University)
A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models (2023), published on arXiv (Cornell University)
Classification by Attention: Scene Graph Classification with Prior Knowledge (2021), published in Proceedings of the AAAI Conference on Artificial Intelligence

Throughout their career, Volker Tresp has collaborated frequently with several researchers. Their most common co-authors include:

Yunpu Ma
Jindong Gu
Sahand Sharifzadeh
Rajat Koner
Marcel Hildebrandt

Best Publications

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

Maximilian Nickel;Volker Tresp;Hans-peter Kriegel

1968
Citations
A Review of Relational Machine Learning for Knowledge Graphs

Maximilian Nickel;Kevin Murphy;Volker Tresp;Evgeniy Gabrilovich

1777
Citations
A Bayesian Committee Machine

Volker Tresp

525
Citations
Learning Gaussian processes from multiple tasks

Kai Yu;Volker Tresp;Anton Schwaighofer

510
Citations
Probabilistic memory-based collaborative filtering

Kai Yu;A. Schwaighofer;V. Tresp;Xiaowei Xu

500
Citations
Factorizing YAGO: scalable machine learning for linked data

Maximilian Nickel;Volker Tresp;Hans-Peter Kriegel

489
Citations
Active learning via transductive experimental design

Kai Yu;Jinbo Bi;Volker Tresp

421
Citations
Method and device for the neuronal modelling of a dynamic system with non-linear stochastic behavior

Thomas Briegel;Volker Tresp

339
Citations
Multi-label informed latent semantic indexing

Kai Yu;Shipeng Yu;Volker Tresp

321
Citations
Representative sampling for text classification using support vector machines

Zhao Xu;Kai Yu;Volker Tresp;Xiaowei Xu

299
Citations
Extraction of semantic biomedical relations from text using conditional random fields

Markus Bundschus;Markus Bundschus;Mathaeus Dejori;Mathaeus Dejori;Martin Stetter;Volker Tresp

295
Citations
Stochastic Relational Models for Discriminative Link Prediction

Kai Yu;Wei Chu;Shipeng Yu;Volker Tresp

272
Citations
Mixtures of Gaussian Processes

Volker Tresp

259
Citations
Natural Language Questions for the Web of Data

Mohamed Yahya;Klaus Berberich;Shady Elbassuoni;Maya Ramanath

252
Citations
Type-Constrained Representation Learning in Knowledge Graphs

Denis Krompaβ;Stephan Baier;Volker Tresp

232
Citations
Learning Gaussian Process Kernels via Hierarchical Bayes

Anton Schwaighofer;Volker Tresp;Kai Yu

225
Citations
Combining Estimators Using Non-Constant Weighting Functions

Volker Tresp;Michiaki Taniguchi

196
Citations
Towards LarKC: A Platform for Web-Scale Reasoning

D. Fensel;F. van Harmelen;B. Andersson;P. Brennan

188
Citations
Supervised probabilistic principal component analysis

Shipeng Yu;Kai Yu;Volker Tresp;Hans-Peter Kriegel

181
Citations
Tensor-train recurrent neural networks for video classification

Yinchong Yang;Denis Krompass;Volker Tresp

180
Citations

Frequent Co-Authors

Shipeng Yu Pinterest

Hans-Peter Kriegel Ludwig-Maximilians-Universität München

Subutai Ahmad Numenta

Hinrich Schütze Ludwig-Maximilians-Universität München

Xiaowei Xu University of Arkansas at Little Rock

Kristian Kersting Technical University of Darmstadt

Thomas Seidl Ludwig-Maximilians-Universität München

Stephan Günnemann Technical University of Munich

Jens Lehmann University of Bonn

Evgeniy Gabrilovich Google (United States)

External Links

Personal website of Volker Tresp Google Scholar page

If you think any of the details on this page are incorrect, let us know.

Related Online Degrees & Career Pathways

Exploring online degrees related to Computer Science opens up a range of options tailored to different career goals. Selecting the best degree to get depends on factors like your interests, desired career path, and commitment level.

If you are seeking flexibility or a quicker entry into the workforce, you might consider programs known as the what is the easiest masters degree to get. These programs can provide valuable skills without the extensive time or workload of more rigorous options.

For those interested in advancing research or pursuing academic leadership, pursuing affordable phd programs online is increasingly accessible. Such programs allow you to continue working while earning a respected credential at a lower cost.

Alternatively, if you're looking for a fast-track to educational leadership, accelerated edd programs online offer the opportunity to complete your degree in less time, often entirely online.

Carefully review program details, accreditation, and your personal career objectives to identify the best fit for your goals in Computer Science or related fields.