Knowledge Graph Representation Learning (KGRL), or Knowledge Graph Embedding
(KGE), is essential for AI applications such as knowledge construction and information …

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 …

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

Abstract Knowledge graph representation learning, which is able to support further
knowledge computation and reasoning, has drawn much research attention in the field of …

Increasing quantities of semantic resources offer a wealth of human knowledge, but their
growth also increases the probability of wrong knowledge base entries. The development of …

Neurosymbolic artificial intelligence (AI) is an increasingly active area of research that
combines symbolic reasoning methods with deep learning to leverage their complementary …

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 …

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 …

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 …

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 …