ZJUKG

Research

The team is devoted to foundational innovations in the fields of Knowledge Graphs, Large Language Models and a variety of their applications.

Knowledge Graphs

Knowledge Graphs (KGs) model world knowledge in graph-like forms to facilitate search, query answering, recommendation, or analytics. Areas of our research include: automatic KG construction, neural reasoning with KG, KG augmented deep models, etc.

Large Language Models

Large Language Model (LLMs) are deep models pre-trained on large-scale corpus with general purposes. Areas of our research include: knowledge-aware prompts, model editing, language models reasoning, low-resource model training, etc.

AI in Science

Language and graph technologies are powerful in modeling complex scientific knowledge in domains such as life science, material science, or social science. We explore the integration of KGs and LLMs to advance AI scientific discovery.

Featured Projects

The team is also committed to develop open KG datasets and open LLM tools for the communities.

StrutLLM

LLM for Structure Data

NeuralKG

Easy Representation Tool for KGs

Milestones

Some recent milestones about the team. Read More...

2023.6

Our LLM framework KnowLM was released

KnowLM is an open-source large-scale knowledgable language model framework
2023.01

Our new Nature Machine Intelligence article was accepted

A new article "Knowledge graph-enhanced molecular contrastive learning with functional prompt. " was just release in official website of Nature Machine Intelligence.
2022.01

OpenBG was released jointly by Zhejiang University and Alibaba

Zhejiang University and Aliaba group joint released OpenBG, an open business knowledge graph for sake of supporting open knoweldge graph research.
2021.11

Best paper awards from both CCKS2021 and IJCKG2021.

The team won the best paper awards from both China Conference on Knowledge Graph and Semantic Computing (CCKS2021) and International Joint Conference on Knowledge Graph(IJCKG2021)