1.7k citations · 2.4k across the 47 of their papers we have counts for
4 papers · 1 filter
Predicting the Co-Evolution of Event and Knowledge Graphs
Cristóbal Esteban, Volker Tresp, Yinchong Yang +2
Embedding learning, a.k.a. representation learning, has been shown to be able to model large-scale semantic knowledge graphs. A key concept is a mapping of the knowledge graph to a…
Learning with Memory Embeddings
Volker Tresp, Cristóbal Esteban, Yinchong Yang +2
Embedding learning, a.k.a. representation learning, has been shown to be able to model large-scale semantic knowledge graphs. A key concept is a mapping of the knowledge graph to a…
Type-Constrained Representation Learning in Knowledge Graphs
Denis Krompaß, Stephan Baier, Volker Tresp
Large knowledge graphs increasingly add value to various applications that require machines to recognize and understand queries and their semantics, as in search or question answer…
A Review of Relational Machine Learning for Knowledge Graphs
Maximilian Nickel, Kevin Murphy, Volker Tresp +1
Relational machine learning studies methods for the statistical analysis of relational, or graph-structured, data. In this paper, we provide a review of how such statistical models…