Knowledge graph (KG) reasoning is becoming increasingly popular in both academia and
industry. Conventional KG reasoning based on symbolic logic is deterministic, with …

Knowledge Graphs (KGs) have become increasingly common for representing large-scale
linked data. However, their immense size has required graph learning systems to assist …

Neurosymbolic AI is an increasingly active area of research which aims to combine symbolic
reasoning methods with deep learning to generate models with both high predictive …

While Knowledge Graphs (KGs) have long been used as valuable sources of structured
knowledge, in recent years, KG embeddings have become a popular way of deriving …

Using knowledge graph embedding models (KGEMs) is a popular approach for predicting
links in knowledge graphs (KGs). Traditionally, the performance of KGEMs for link prediction …

Due to the rapid growth of knowledge graphs (KG) as representational learning methods in
recent years, question-answering approaches have received increasing attention from …

Knowledge bases are typically incomplete, meaning that they are missing information that
we would expect to be there. Recent years have seen two main approaches to guess …

Abstract Knowledge graph embedding models (KGEMs) are used for various tasks related to
knowledge graphs (KGs), including link prediction. They are trained with loss functions that …

Graph data is omnipresent and has a wide variety of applications, such as in natural
science, social networks, or the semantic web. However, while being rich in information …

Ontological reasoning has great prospects in applications based on domain-specific
knowledge graphs (KG). However, it is difficult for existing logic reasoners to quickly perform …