Knowledge graphs and semantic technologies provide powerful frameworks for representing, integrating, and reasoning over complex biomedical information. Unlike unstructured data or simple tabular formats, knowledge graphs explicitly capture entities such as diseases, drugs, genes, proteins and patients, as well as their multifaceted relationships, in a structured, semantically rich format. This enables sophisticated reasoning, inference, and knowledge discovery that goes beyond statistical pattern recognition. This special session focuses on the development and application of knowledge graphs and semantic web technologies in biomedicine. From constructing comprehensive drug-disease-gene networks to building patient-specific knowledge representations from electronic health records, these approaches enable the integration of heterogeneous data sources while preserving semantic meaning and supporting explainable reasoning. Temporal knowledge graphs capture the dynamic nature of disease progression and treatment responses, while knowledge graph embeddings enable predictive tasks such as drug repurposing and biomarker discovery. We invite contributions that advance the state-of-the-art in biomedical knowledge representation and semantic data integration, while enabling interpretable reasoning, knowledge discovery, and intelligent decision support across biomedical research and clinical practice.