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 …

随着图神经网络的发展, 基于图神经网络的知识图谱嵌入方法日益受到研究人员的关注.
相比传统的方法, 它可以更好地处理实体的多样性和复杂性, 并捕捉实体的多重特征和复杂关系 …

Abstract Knowledge graph completion (KGC) aims to predict the missing element in a triple
based on known triples or facts. Recently, plenty of representation learning methods for KGC …

Many commonsense knowledge graphs (CKGs) still suffer from incompleteness although
they have been applied in many natural language processing tasks successfully. Due to the …

A vast amount of textual and structural information is required for knowledge graph
construction and its downstream tasks. However, most of the current knowledge graphs are …

The regulatory compliance supervision of listed enterprises is of great significance for
ensuring the stable operation of financial markets. However, existing graph propagation …

Learning disentangled entity representations has garnered significant attention in the field of
knowledge graph completion (KGC). However, the existing methods inherently overlook the …

As a building block of knowledge acquisition, knowledge graph completion (KGC) aims at
inferring missing facts in knowledge graphs (KGs) automatically. Previous studies mainly …

Abstract Knowledge Base Embedding (KBE) models have been widely used to encode
structured information from knowledge bases, including WordNet. However, the existing …

This vision paper proposes KGNet, an on-demand graph machine learning (GML) as a
service on top of RDF engines to support GML-enabled SPARQL queries. KGNet automates …