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

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 …

Computer-aided process planning is the bridge between computer-aided design and
computer-aided manufacturing. With the advent of the intelligent manufacturing era, process …

Abstract Knowledge graphs are organized as triplets to represent facts from the real world
and play an important role in various intelligent information systems. Because knowledge …

One of the most effective ways to solve the problem of knowledge graph completion is
embedding-based models. Graph neural networks (GNNs) are popular and promising …

Reasoning on knowledge graph (KG) has been studied for explainable recommendation
due to its ability of providing explicit explanations. However, current KG-based explainable …

Graph neural networks (GNNs) aim to learn well-trained representations in a lower-
dimension space for downstream tasks while preserving the topological structures. In recent …

Abstract Knowledge graph completion (KGC) aims to infer missing links between entities
based on the observed ones. Current KGC methods primarily focus on KG embedding …

Abstract Knowledge graph representation learning (KGRL) aims to infer the missing links
between target entities based on existing triples. Graph neural networks (GNNs) have been …

With the continuous emergence of new knowledge, Knowledge Graph (KG) typically suffers
from the incompleteness problem, hindering the performance of downstream applications …