Large Language Models (LLMs), e.g., ChatGPT, PaLM, and LLaMA are transforming natural language processing (NLP) and artificial intelligence (AI). Recent LLMs browse Web knowledge and learn from external knowledge bases, unifying LLMs and knowledge graphs (KGs). The possibility of bridging KGs with LLMs has garnered attention in knowledge engineering. On the one hand, LLMs can be enhanced with KGs to provide answers with more contextualized facts. On the other hand, downstream tasks, e.g., KG curation, embedding, and search can also benefit by adopting LLMs. It remains an interesting direction to explore effective interactions between LLMs and KGs, where many recent advances arise from NLP, deep learning, information retrieval, and computer vision domains. The workshop, titled “LLM+KG: Data Management Opportunities in Unifying Large Language Models + Knowledge Graphs”, is targeted at data management researchers, aiming to discuss interesting opportunities, e.g., data cleaning, modeling, designing of algorithms and systems, scalability, fairness, privacy, usability, and explanation
