Knowledge graph embedding (KGE) aims at learning powerful representations to benefit
various artificial intelligence applications. Meanwhile, contrastive learning has been widely …

Semantic embedding of knowledge graphs has been widely studied and used for prediction
and statistical analysis tasks across various domains such as Natural Language Processing …

The embeddings of entities in a large knowledge base (eg, Wikipedia) are highly beneficial
for solving various natural language tasks that involve real world knowledge. In this paper …

Abstract Knowledge graph embeddings are now a widely adopted approach to knowledge
representation in which entities and relationships are embedded in vector spaces. In this …

Understanding traffic scenes requires considering heterogeneous information about
dynamic agents and the static infrastructure. In this work we propose SCENE, a …

Abstract Knowledge graphs have shown to be highly beneficial to recommender systems,
providing an ideal data structure to generate hybrid recommendations using both content …

Deep learning techniques are increasingly being applied to solve various machine learning
tasks that use Knowledge Graphs as input data. However, these techniques typically learn a …

Although several large knowledge graphs have been proposed in the scholarly field, such
graphs are limited with respect to several data quality dimensions such as accuracy and …