Recent Highlights
- GRAPES 🍇 has been accepted in Transactions on Machine Learning Research! we propose a method for scaling GNNs to graphs with up to 2M nodes and 60M edges.
- I have defended my PhD with distinction cum laude. My thesis is avalable at this link.
- UnRavL has been accepted to the 2024 Learning on Graphs conference!
- Our paper, Adapting Neural Link Predictors for Data-Efficient Complex Query Answering has been accepted to NeurIPS 2023!
Selected Publications
For a full list of publications, see Google Scholar.
GRAPES: Learning to Sample Graphs for Scalable Graph Neural Networks
Taraneh Younesian, Daniel Daza, Emile van Krieken, Thiviyan Thanapalasingam, Peter Bloem, in Transactions on Machine Learning Research (2025).
[paper] [code]
UnRavL: A Neuro-Symbolic Framework for Answering Graph Pattern Queries in Knowledge Graphs
Tamara Cucumides, Daniel Daza, Pablo Barcelo, Michael Cochez, Floris Geerts, Juan L Reutter, Miguel Romero Orth, in Learning on Graphs Conference 2024.
[paper]
Adapting Neural Link Predictors for Data-Efficient Complex Query Answering
Erik Arakelyan, Pasquale Minervini, Daniel Daza, Michael Cochez, and Isabelle Augenstein, in NeurIPS 2024.
[arxiv] [code]
BioBLP: A Modular Framework for Learning on Multimodal Biomedical Knowledge Graphs
Daniel Daza, Dimitrios Alivanistos, Payal Mitra, Thom Pijnenburg, Michael Cochez, and Paul Groth, in Journal of Biomedical Semantics (2023).
[paper] [code]
SlotGAN: Detecting Mentions in Text via Adversarial Distant Learning
Daniel Daza, Michael Cochez, and Paul Groth, in ACL 2022 Workshop on Structured Prediction for NLP.
[paper] [code] [bibtex]
Inductive Entity Representations from Text via Link Prediction
Daniel Daza, Michael Cochez, and Paul Groth, in The Web Conference 2021.
[arXiv] [code] [bibtex]