Unifying large language models and knowledge graphs: A roadmap
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 …
field of natural language processing and artificial intelligence, due to their emergent ability …
Time-llm: Time series forecasting by reprogramming large language models
Time series forecasting holds significant importance in many real-world dynamic systems
and has been extensively studied. Unlike natural language process (NLP) and computer …
and has been extensively studied. Unlike natural language process (NLP) and computer …
Llms for knowledge graph construction and reasoning: Recent capabilities and future opportunities
This paper presents an exhaustive quantitative and qualitative evaluation of Large
Language Models (LLMs) for Knowledge Graph (KG) construction and reasoning. We …
Language Models (LLMs) for Knowledge Graph (KG) construction and reasoning. We …
Reasoning on graphs: Faithful and interpretable large language model reasoning
Large language models (LLMs) have demonstrated impressive reasoning abilities in
complex tasks. However, they lack up-to-date knowledge and experience hallucinations …
complex tasks. However, they lack up-to-date knowledge and experience hallucinations …
Towards self-interpretable graph-level anomaly detection
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 …
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
Graph learning plays a pivotal role and has gained significant attention in various
application scenarios, from social network analysis to recommendation systems, for its …
application scenarios, from social network analysis to recommendation systems, for its …
Graph machine learning in the era of large language models (llms)
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 …
social networks, knowledge graphs, and molecular discovery. With the advent of deep …
MADE: Multicurvature Adaptive Embedding for Temporal Knowledge Graph Completion
Temporal knowledge graphs (TKGs) are receiving increased attention due to their time-
dependent properties and the evolving nature of knowledge over time. TKGs typically …
dependent properties and the evolving nature of knowledge over time. TKGs typically …
Large Language Models-guided Dynamic Adaptation for Temporal Knowledge Graph Reasoning
Temporal Knowledge Graph Reasoning (TKGR) is the process of utilizing temporal
information to capture complex relations within a Temporal Knowledge Graph (TKG) to infer …
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
This survey investigates the synergistic relationship between Large Language Models
(LLMs) and Knowledge Graphs (KGs), which is crucial for advancing AI's capabilities in …
(LLMs) and Knowledge Graphs (KGs), which is crucial for advancing AI's capabilities in …