We investigate the problem of learning continuous vector representations of knowledge
graphs for predicting missing links. Recent results suggest that using a Hermitian inner …

Abstract Knowledge graph embedding research has mainly focused on the two smallest
normed division algebras, $\mathbb {R} $ and $\mathbb {C} $. Recent results suggest that …

Knowledge graph completion refers to predicting missing triples. Most approaches achieve
this goal by predicting entities, given an entity and a relation. We predict missing triples via …

An increasing amount of software with machine learning components is being deployed.
This poses the question of quality assurance for such components: how can we validate …

Knowledge graph embedding techniques are key to making knowledge graphs amenable to
the plethora of machine learning approaches based on vector representations. Link …

Abstract Machine learning (ML) based software systems are increasingly being used in
several application domains affecting our daily lives substantially. In many of those domains …

In recent years, we observe an increasing amount of software with machine learning
components being deployed. This poses the question of quality assurance for such …