Lung cancer is one of the deadliest types of cancer and poses a significant public health challenge. Despite numerous studies identifying various risk factors associated with this disease, further research remains essential, particularly in the biological domain. Currently, multiple data sources compile biological information on various diseases, including lung cancer and its subtypes. However, these sources often differ in structure and format, making data extraction and efficient use in artificial intelligence (AI) models more challenging. Ontologies, semantic technologies, and data reuse strategies play a crucial role in addressing this issue. By leveraging these approaches, it is possible to build a knowledge graph that integrates heterogeneous data sources into a unified format, facilitating interoperability and data extraction. Lung-CABO is an ontology specifically designed for lung cancer and its subtypes, whose effectiveness has been evaluated. Through this ontology, a knowledge graph has been developed to explore, extract, and utilize information both to identify risk factors and as input for AI models. Additionally, Lung-CABO is reusable and can be expanded by incorporating association classes that integrate other relevant data related to the disease, such as environmental factors, further enhancing its scope and applicability.