A survey on knowledge graphs: Representation, acquisition, and applications
Human knowledge provides a formal understanding of the world. Knowledge graphs that
represent structural relations between entities have become an increasingly popular …
represent structural relations between entities have become an increasingly popular …
A review: Knowledge reasoning over knowledge graph
X Chen, S Jia, Y Xiang - Expert systems with applications, 2020 - Elsevier
Mining valuable hidden knowledge from large-scale data relies on the support of reasoning
technology. Knowledge graphs, as a new type of knowledge representation, have gained …
technology. Knowledge graphs, as a new type of knowledge representation, have gained …
Graph neural networks: foundation, frontiers and applications
The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …
recent years. Graph neural networks, also known as deep learning on graphs, graph …
Graph learning: A survey
Graphs are widely used as a popular representation of the network structure of connected
data. Graph data can be found in a broad spectrum of application domains such as social …
data. Graph data can be found in a broad spectrum of application domains such as social …
Knowledge graph completion: A review
Z Chen, Y Wang, B Zhao, J Cheng, X Zhao… - Ieee …, 2020 - ieeexplore.ieee.org
Knowledge graph completion (KGC) is a hot topic in knowledge graph construction and
related applications, which aims to complete the structure of knowledge graph by predicting …
related applications, which aims to complete the structure of knowledge graph by predicting …
DKN: Deep knowledge-aware network for news recommendation
Online news recommender systems aim to address the information explosion of news and
make personalized recommendation for users. In general, news language is highly …
make personalized recommendation for users. In general, news language is highly …
Knowledge graph embedding: A survey of approaches and applications
Knowledge graph (KG) embedding is to embed components of a KG including entities and
relations into continuous vector spaces, so as to simplify the manipulation while preserving …
relations into continuous vector spaces, so as to simplify the manipulation while preserving …
Complex embeddings for simple link prediction
In statistical relational learning, the link prediction problem is key to automatically
understand the structure of large knowledge bases. As in previous studies, we propose to …
understand the structure of large knowledge bases. As in previous studies, we propose to …
Openke: An open toolkit for knowledge embedding
We release an open toolkit for knowledge embedding (OpenKE), which provides a unified
framework and various fundamental models to embed knowledge graphs into a continuous …
framework and various fundamental models to embed knowledge graphs into a continuous …