A survey on knowledge graphs: Representation, acquisition, and applications

S Ji, S Pan, E Cambria, P Marttinen… - IEEE transactions on …, 2021 - ieeexplore.ieee.org
Human knowledge provides a formal understanding of the world. Knowledge graphs that
represent structural relations between entities have become an increasingly popular …

A comprehensive survey on automatic knowledge graph construction

L Zhong, J Wu, Q Li, H Peng, X Wu - ACM Computing Surveys, 2023 - dl.acm.org
Automatic knowledge graph construction aims at manufacturing structured human
knowledge. To this end, much effort has historically been spent extracting informative fact …

Unifying large language models and knowledge graphs: A roadmap

S Pan, L Luo, Y Wang, C Chen… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Large language models (LLMs), such as ChatGPT and GPT4, are making new waves in the
field of natural language processing and artificial intelligence, due to their emergent ability …

Graphadapter: Tuning vision-language models with dual knowledge graph

X Li, D Lian, Z Lu, J Bai, Z Chen… - Advances in Neural …, 2023 - proceedings.neurips.cc
Adapter-style efficient transfer learning (ETL) has shown excellent performance in the tuning
of vision-language models (VLMs) under the low-data regime, where only a few additional …

Prodigy: Enabling in-context learning over graphs

Q Huang, H Ren, P Chen, G Kržmanc… - Advances in …, 2023 - proceedings.neurips.cc
In-context learning is the ability of a pretrained model to adapt to novel and diverse
downstream tasks by conditioning on prompt examples, without optimizing any parameters …

Inductive relation prediction by subgraph reasoning

K Teru, E Denis, W Hamilton - International Conference on …, 2020 - proceedings.mlr.press
The dominant paradigm for relation prediction in knowledge graphs involves learning and
operating on latent representations (ie, embeddings) of entities and relations. However …

A comprehensive overview of knowledge graph completion

T Shen, F Zhang, J Cheng - Knowledge-Based Systems, 2022 - Elsevier
Abstract Knowledge Graph (KG) provides high-quality structured knowledge for various
downstream knowledge-aware tasks (such as recommendation and intelligent question …

Aligraph: A comprehensive graph neural network platform

R Zhu, K Zhao, H Yang, W Lin, C Zhou, B Ai… - arxiv preprint arxiv …, 2019 - arxiv.org
An increasing number of machine learning tasks require dealing with large graph datasets,
which capture rich and complex relationship among potentially billions of elements. Graph …

Structure-augmented text representation learning for efficient knowledge graph completion

B Wang, T Shen, G Long, T Zhou, Y Wang… - Proceedings of the Web …, 2021 - dl.acm.org
Human-curated knowledge graphs provide critical supportive information to various natural
language processing tasks, but these graphs are usually incomplete, urging auto …

Meta relational learning for few-shot link prediction in knowledge graphs

M Chen, W Zhang, W Zhang, Q Chen… - arxiv preprint arxiv …, 2019 - arxiv.org
Link prediction is an important way to complete knowledge graphs (KGs), while embedding-
based methods, effective for link prediction in KGs, perform poorly on relations that only …