Knowledge graphs (KGs), a structured representation of human knowledge, have emerged as pivotal tools in organizing vast, interconnected information for machine understanding. This chapter traces the evolution of KGs from their theoretical roots in graph theory and early semantic web initiatives to modern applications in artificial intelligence. Key milestones include large-scale projects like DBpedia and Google’s Knowledge Graph, and the rise of query languages. The chapter details KG construction methodologies, contrasting top-down (ontology-driven) and bottom-up (data-driven) approaches, alongside techniques for knowledge extraction, fusion, and quality assessment. It highlights challenges in integrating heterogeneous data, entity alignment, and maintaining dynamic updates. Applications span semantic search, intelligent question answering, and biomedical innovation. Notably, biomedical KGs integrate entities such as genes, diseases, and drugs to accelerate drug discovery. Case studies demonstrate KG-driven tasks, such as predicting drug-target interactions using graph neural networks. Despite all the progress, challenges persist: standardized workflows, data quality evaluation, and mitigating human bias in biological datasets. This chapter not only synthesizes foundational concepts and cutting-edge techniques but also illustrates KGs’ transformative potential in biomedicine and AI. For researchers and practitioners, it offers a comprehensive roadmap to leverage KGs in biomedical research, making it essential reading for those exploring the intersection of structured knowledge and data-driven modeling.