Human knowledge provides a formal understanding of the world. Knowledge graphs that
represent structural relations between entities have become an increasingly popular …

Abstract Knowledge graph reasoning is the fundamental component to support machine
learning applications such as information extraction, information retrieval, and …

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 …

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

In recent years, with the rapid development of Internet technology and applications, the
scale of Internet data has exploded, which contains a significant amount of valuable …

Graph Neural Networks (GNNs) are often used to realise learnable transformations of graph
data. While effective in practice, GNNs make predictions via numeric manipulations in an …

Rule mining is an effective approach for reasoning over knowledge graph (KG). Existing
works mainly concentrate on mining rules. However, there might be several rules that could …

We propose a principled method to learn a set of human-readable logic rules to explain
temporal point processes. We assume that the generative mechanisms underlying the …

Multi-hop reasoning is an effective and explainable approach to predicting missing facts in
Knowledge Graphs (KGs). It usually adopts the Reinforcement Learning (RL) framework and …

Despite the recent success of end-to-end deep neural networks, there are growing concerns
about their lack of logical reasoning abilities, especially on cognitive tasks with perception …