Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Massih-Reza Amini,Stéphane Canu,Grigorios Tsoumaka Conference p

Allege

innovation

KEGEL

Understanding the?Benefits of?Forgetting When Learning on?Dynamic Graphsn node representations, also called embeddings, that allow to capture in the best way possible the properties of these graphs. More recently, learning node embeddings for dynamic graphs attracted significant interest due to the rich temporal information that they provide about the appearance of edge

CRUC

nutrition

Neolithic

Joint Learning of?Hierarchical Community Structure and?Node Representations: An Unsupervised Approac. Research has shown that many natural graphs can be organized in hierarchical communities, leading to approaches that use these communities to improve the quality of node representations. However, these approaches do not take advantage of the learned representations to also improve the quality of t

哪有黃油

邊緣

Enhance Temporal Knowledge Graph Completion via?Time-Aware Attention Graph Convolutional Network graph is far from consummation because of its late start. Recent researches have shifted to the temporal knowledge graph relying on the extension of static ones. Most of these methods seek approaches to incorporate temporal information but neglect the potential adjacent impact merged in temporal kn

AER

Start Small, Think Big: On Hyperparameter Optimization for Large-Scale Knowledge Graph Embeddingsown that KGE models are sensitive to hyperparameter settings, however, and that suitable choices are dataset-dependent. In this paper, we explore hyperparameter optimization (HPO) for very large knowledge graphs, where the cost of evaluating individual hyperparameter configurations is excessive. Pri

RODE