Recent Activity
- New preprint: with GRAPES 🍇 we propose a method for scaling GNNs to graphs with up to 2M nodes and 60M edges.
- Our paper, Adapting Neural Link Predictors for Data-Efficient Complex Query Answering has been accepted to NeurIPS 2024!
- We're presenting Harnessing the Web and Knowledge Graphs for Automated Impact Investing Scoring at the KDD 2023 Workshop on AI for Climate Sustainability.
- New preprint! With BioBLP we propose to learn embeddings in biomedical knowledge graphs by incorporating multimodal data of proteins, molecules, and diseases.
- I've started a research internship at the Bosch Center for Artificial Intelligence, where I will work on topics related to representation learning on graphs.
- Our paper, SlotGAN: Detecting Mentions in Text via Adversarial Distant Learning has been presented at the ACL 2022 Workshop on Structured Prediction for NLP.
- I've presented Inductive Entity Representations from Text via Link Prediction at the Web Conference 2021.
- Our paper, Complex Query Answering with Neural Link Predictors, has received an Outsdanding Paper Award at ICLR 2021! (see announcement.)
- New blogpost on Normalizing Flows.
- I've written a report for my virtual attendance to AKBC 2020.
- Our paper, Message Passing Query Embedding, has been accepted at the ICML 2020 GRL+ Workshop!
Publications
GRAPES: Learning to Sample Graphs for Scalable Graph Neural Networks
Taraneh Younesian, Thiviyan Thanapalasingam, Emile van Krieken, Daniel Daza, Peter Bloem (preprint).
[arxiv] [code]
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]
Harnessing the Web and Knowledge Graphs for Automated Impact Investing Scoring
Qingzhi Hu, Daniel Daza, Laurens Swinkels, Kristina Ūsaitė, Robbert-Jan ‘t Hoen, and Paul Groth, in KDD 2023 Workshop on AI for Climate Sustainability.
[arxiv]
BioBLP: A Modular Framework for Learning on Multimodal Biomedical Knowledge Graphs
Daniel Daza, Dimitrios Alivanistos, Payal Mitra, Thom Pijnenburg, Michael Cochez, and Paul Groth (preprint).
[arXiv] [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]
Complex Query Answering with Neural Link Predictors
Erik Arakelyan, Daniel Daza, Pasquale Minervini, and Michael Cochez, in ICLR 2021 (🏆 Outstanding Paper Award).
[arXiv] [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]
Approximate Knowledge Graph Query Answering: From Ranking to Binary Classification
Ruud van Bakel, Teodor Aleksiev, Daniel Daza, Dimitrios Alivanistos, and Michael Cochez, in International Workshop on Graph Structures for Knowledge Representation and Reasoning, 2020.
[arXiv] [bibtex]
Message Passing Query Embedding
Daniel Daza and Michael Cochez, in ICLR 2020 Workshop on Graph Representation Learning and Beyond.
[arXiv] [code] [bibtex]
A Modular Framework for Unsupervised Graph Representation Learning
Daniel Daza, Master's Thesis (2019), supervisor: Thomas Kipf.
[pdf]