Many mathematical models have been leveraged to design embeddings for representing
Knowledge Graph (KG) entities and relations for link prediction and many downstream tasks …

Abstract Representation learning approaches for knowledge graphs have been mostly
designed for static data. However, many knowledge graphs involve evolving data, eg, the …

Entity alignment aims to identify equivalent entity pairs between different knowledge graphs
(KGs). Recently, the availability of temporal KGs (TKGs) that contain time information created …

In the last few years, there has been a surge of interest in learning representations of
entitiesand relations in knowledge graph (KG). However, the recent availability of temporal …

Temporal knowledge graph embedding (TKGE) models are commonly utilized to infer the
missing facts and facilitate reasoning and decision-making in temporal knowledge graph …

Recent years, Knowledge Graph Embeddings (KGEs) have shown promising performance
on link prediction tasks by mapping the entities and relations from a Knowledge Graph (KG) …

Predicting new links in complex networks can have a large societal impact. In fact, many
complex systems can be modeled through networks, and the meaning of the links depend …

Knowledge graphs (KGs) are widely used for modeling scholarly communication, performing
scientometric analyses, and supporting a variety of intelligent services to explore the …

Knowledge graphs (KGs) represent world facts in a structured form. Although knowledge
graphs are quantitatively huge and consist of millions of triples, the coverage is still only a …

Knowledge graphs' reasoning is of great significance for the further development of artificial
intelligence and information retrieval, especially for reasoning over temporal knowledge …