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

Knowledge graph reasoning (KGR), aiming to deduce new facts from existing facts based on
mined logic rules underlying knowledge graphs (KGs), has become a fast-growing research …

With Knowledge Graphs (KGs) at the center of numerous applications such as recommender
systems and question-answering, the need for generalized pipelines to construct and …

Foundation models in language and vision have the ability to run inference on any textual
and visual inputs thanks to the transferable representations such as a vocabulary of tokens …

Inductive knowledge graph completion has been considered as the task of predicting
missing triplets between new entities that are not observed during training. While most …

Socioeconomic indicators reflect location status from various aspects such as
demographics, economy, crime and land usage, which play an important role in the …

With knowledge graphs (KGs) at the center of numerous applications such as recommender
systems and question answering, the need for generalized pipelines to construct and …

Multimodal knowledge graphs (MKGs), which intuitively organize information in various
modalities, can benefit multiple practical downstream tasks, such as recommendation …

Formulating and answering logical queries is a standard communication interface for
knowledge graphs (KGs). Alleviating the notorious incompleteness of real-world KGs, neural …

Transformers have achieved remarkable performance in widespread fields, including
natural language processing, computer vision and graph mining. However, vanilla …