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 …

Time-llm: Time series forecasting by reprogramming large language models

M Jin, S Wang, L Ma, Z Chu, JY Zhang, X Shi… - arXiv preprint arXiv …, 2023 - arxiv.org
Time series forecasting holds significant importance in many real-world dynamic systems
and has been extensively studied. Unlike natural language process (NLP) and computer …

Llms for knowledge graph construction and reasoning: Recent capabilities and future opportunities

Y Zhu, X Wang, J Chen, S Qiao, Y Ou, Y Yao, S Deng… - World Wide Web, 2024 - Springer
This paper presents an exhaustive quantitative and qualitative evaluation of Large
Language Models (LLMs) for Knowledge Graph (KG) construction and reasoning. We …

Reasoning on graphs: Faithful and interpretable large language model reasoning

L Luo, YF Li, G Haffari, S Pan - arXiv preprint arXiv:2310.01061, 2023 - arxiv.org
Large language models (LLMs) have demonstrated impressive reasoning abilities in
complex tasks. However, they lack up-to-date knowledge and experience hallucinations …

Towards self-interpretable graph-level anomaly detection

Y Liu, K Ding, Q Lu, F Li… - Advances in Neural …, 2024 - proceedings.neurips.cc
Graph-level anomaly detection (GLAD) aims to identify graphs that exhibit notable
dissimilarity compared to the majority in a collection. However, current works primarily focus …

Graph Learning under Distribution Shifts: A Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learning

M Wu, X Zheng, Q Zhang, X Shen, X Luo, X Zhu… - arXiv preprint arXiv …, 2024 - arxiv.org
Graph learning plays a pivotal role and has gained significant attention in various
application scenarios, from social network analysis to recommendation systems, for its …

Graph machine learning in the era of large language models (llms)

W Fan, S Wang, J Huang, Z Chen, Y Song… - arXiv preprint arXiv …, 2024 - arxiv.org
Graphs play an important role in representing complex relationships in various domains like
social networks, knowledge graphs, and molecular discovery. With the advent of deep …

MADE: Multicurvature Adaptive Embedding for Temporal Knowledge Graph Completion

J Wang, B Wang, J Gao, S Pan, T Liu… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Temporal knowledge graphs (TKGs) are receiving increased attention due to their time-
dependent properties and the evolving nature of knowledge over time. TKGs typically …

Large Language Models-guided Dynamic Adaptation for Temporal Knowledge Graph Reasoning

J Wang, K Sun, L Luo, W Wei, Y Hu, AWC Liew… - arXiv preprint arXiv …, 2024 - arxiv.org
Temporal Knowledge Graph Reasoning (TKGR) is the process of utilizing temporal
information to capture complex relations within a Temporal Knowledge Graph (TKG) to infer …

Research Trends for the Interplay between Large Language Models and Knowledge Graphs

H Khorashadizadeh, FZ Amara, M Ezzabady… - arXiv preprint arXiv …, 2024 - arxiv.org
This survey investigates the synergistic relationship between Large Language Models
(LLMs) and Knowledge Graphs (KGs), which is crucial for advancing AI's capabilities in …