Information extraction methods proved to be effective at triple extraction from structured or
unstructured data. The organization of such triples in the form of (head entity, relation, tail …

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

Abstract Motivation Variational autoencoders (VAEs) have rapidly increased in popularity in
biological applications and have already successfully been used on many omic datasets …

Abstract Knowledge graphs have been receiving increasing attention from researchers.
However, most of these graphs are incomplete, leading to the rise of knowledge graph …

Graph Neural Networks (GNNs) are prominent in graph machine learning and have shown
state-of-the-art performance in Link Prediction (LP) tasks. Nonetheless, recent studies show …

Graphs provide a natural, intuitive, and holistic means to capture relationships between
different text elements in Natural Language Processing (NLP) such as words, sentences …

The paper aims to explore the untapped potential of hypergraphs by leveraging attribute-rich
and multi-layered structures. The primary objective is to develop an innovative learning …

Aiming to accurately predict missing edges representing relations between entities, which
are pervasive in real-world Knowledge Graphs (KGs), relation prediction plays a critical role …

Abstract Graph Neural Networks (GNNs) are commonly applied to analyze real-world graph-
structured data. However, GNNs are sensitive to the given graph structure, which cast …

Abstract Knowledge graph reasoning is an important problem for knowledge graphs. In this
paper, we propose a novel and principled framework called RulE (stands for Rule …