Bridging Data and Discovery: A Survey on Knowledge Graphs in AI for Science

Abstract Knowledge graphs have emerged as a powerful paradigm for structuring, organizing, and reasoning over complex scientific knowledge, and are increasingly recognized as catalysts for accelerating AI for science. This study provides a comprehensive survey of Scientific Knowledge Graphs (SciKGs), covering their construction methodologies and diverse applications across biology, chemistry, and materials science. We examine how SciKGs support tasks such as drug development, omics analysis, reaction prediction, and materials design, and highlight how the synergistic integration of SciKGs and large language models (LLMs) forms a knowledge- and language-driven framework for scientific discovery, in which SciKGs serve as the foundational knowledge infrastructure and LLMs act as dynamic semantic engines. We further identify key challenges and outline emerging opportunities toward building auditable, interoperable, and self-evolving SciKGs. Looking forward, we envision a new generation of SciKG-centered ecosystems where self-updating graphs, co-evolving with LLMs and embodied within AI scientists, become core infrastructures that autonomously drive, verify, and accelerate scientific discovery.