Large language models (LLMs), such as ChatGPT and GPT-4, are versatile and can solve
different tasks due to their emergent ability and generalizability. However, LLMs sometimes …

This paper shows how to construct knowledge graphs (KGs) from pre-trained language
models (eg, BERT, GPT-2/3), without human supervision. Popular KGs (eg, Wikidata, NELL) …

Large language models (LLMs) have shown remarkable generalization capability with
exceptional performance in various language modeling tasks. However, they still exhibit …

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 …

This paper presents an exhaustive quantitative and qualitative evaluation of Large
Language Models (LLMs) for Knowledge Graph (KG) construction and reasoning. We …

Graph walking based on reinforcement learning (RL) has shown great success in navigating
an agent to automatically complete various reasoning tasks over an incomplete knowledge …

While large language models (LLMs) have made considerable advancements in
understanding and generating unstructured text, their application in structured data remains …

Multi-hop reasoning is an effective and explainable approach to predicting missing facts in
Knowledge Graphs (KGs). It usually adopts the Reinforcement Learning (RL) framework and …

Large language models (LLMs) have demonstrated human-level performance on a vast
spectrum of natural language tasks. However, it is largely unexplored whether they can …

In recent years, reasoning over knowledge graphs (KGs) has been widely adapted to
empower retrieval systems, recommender systems, and question answering systems …