In this article, we provide a comprehensive introduction to knowledge graphs, which have
recently garnered significant attention from both industry and academia in scenarios that …

Abstract Knowledge Graph (KG) provides high-quality structured knowledge for various
downstream knowledge-aware tasks (such as recommendation and intelligent question …

Link prediction is a very fundamental task on graphs. Inspired by traditional path-based
methods, in this paper we propose a general and flexible representation learning framework …

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 …

The dominant paradigm for relation prediction in knowledge graphs involves learning and
operating on latent representations (ie, embeddings) of entities and relations. However …

This paper studies learning logic rules for reasoning on knowledge graphs. Logic rules
provide interpretable explanations when used for prediction as well as being able to …

Scientists aim to discover meaningful formulae that accurately describe experimental data.
Mathematical models of natural phenomena can be manually created from domain …

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 …

Abstract Knowledge graph completion (KGC) aims to infer missing facts based on the
observed ones, which is significant for many downstream applications. Given the success of …

The aim of knowledge graph (KG) completion is to extend an incomplete KG with missing
triples. Popular approaches based on graph embeddings typically work by first representing …