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 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.
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.
KnowLM is an open-source large-scale knowledgable language model framework
A new article "Knowledge graph-enhanced molecular contrastive learning with functional prompt. " was just release in official website of Nature Machine Intelligence.
Zhejiang University and Aliaba group joint released OpenBG, an open business knowledge graph for sake of supporting open knoweldge graph research.
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)
Full Professor, College of Computer Science and Technology, Zhejiang University
Director, ZJU-Alibaba-AntGroup Joint Center for Knowledge Engine
Leading Founder, OpenKG Initiative
Associate Professor
College of Software, Zhejiang University
ZJU-Top100 Tantent Researcher
Assistant Professor
College of Software, Zhejiang University
Associate Professor
College of Computer Science, Zhejiang University
HIC-ZJU Tantent Researcher
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