83 citations · 205 across the 16 of their papers we have counts for
17 papers
SUBMASSIVE: Resolving Subclass Cycles in Very Large Knowledge Graphs
Shuai Wang, Peter Bloem, Joe Raad +1
Large knowledge graphs capture information of a large number of entities and their relations. Among the many relations they capture, class subsumption assertions are usually presen…
End-to-End Learning on Multimodal Knowledge Graphs
W. X. Wilcke, P. Bloem, V. de Boer +1
Knowledge graphs enable data scientists to learn end-to-end on heterogeneous knowledge. However, most end-to-end models solely learn from the relational information encoded in grap…
IntelliGraphs: Datasets for Benchmarking Knowledge Graph Generation
Thiviyan Thanapalasingam, Emile van Krieken, Peter Bloem +1
Knowledge Graph Embedding (KGE) models are used to learn continuous representations of entities and relations. A key task in the literature is predicting missing links between enti…
Relational Graph Convolutional Networks: A Closer Look
Thiviyan Thanapalasingam, Lucas van Berkel, Peter Bloem +1
In this paper, we describe a reproduction of the Relational Graph Convolutional Network (RGCN). Using our reproduction, we explain the intuition behind the model. Our reproduction…
Finding Motifs in Knowledge Graphs using Compression
Peter Bloem
We introduce a method to find network motifs in knowledge graphs. Network motifs are useful patterns or meaningful subunits of the graph that recur frequently. We extend the common…
Uncertainty Intervals for Graph-based Spatio-Temporal Traffic Prediction
Tijs Maas, Peter Bloem
Many traffic prediction applications rely on uncertainty estimates instead of the mean prediction. Statistical traffic prediction literature has a complete subfield devoted to unce…