Automatic knowledge graph construction aims at manufacturing structured human
knowledge. To this end, much effort has historically been spent extracting informative fact …

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 Graphs (KGs) have found many applications in industrial and in academic
settings, which in turn, have motivated considerable research efforts towards large-scale …

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

Most of todays work on knowledge graph completion is concerned with sub-symbolic
approaches that focus on the concept of embedding a given graph in a low dimensional …

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 …

Reasoning on the knowledge graph (KG) aims to infer new facts from existing ones.
Methods based on the relational path have shown strong, interpretable, and transferable …

As a powerful expression of human knowledge in a structural form, knowledge graph (KG)
has drawn great attention from both the academia and the industry and a large number of …

In the active research area of employing embedding models for knowledge graph
completion, particularly for the task of link prediction, most prior studies used two benchmark …