构建可复用的伦理决策模型框架，支持机器理解具体情境下的道德判断。

Applied ethics applies ethical decision-making to domain-specific contexts using contextual information such as agents, actions, temporal and spatial settings, and theoretical constructs such as utility, virtues, rights, and duties. However, representing an ethical decision is challenging as it may be abstract, context-sensitive, and semantically heterogeneous. Nevertheless, important ethical and contextual factors can be formally modeled to support structured ethical reasoning. Knowledge representation and reasoning provide a mechanism to translate abstract ethical concepts into machine-interpretable conceptual structures in the context of an event. To achieve this, we propose ApplE, an Applied Ethics ontology that models ethical theory and event context within a unified and modular conceptual framework for ethical decision-making. The ontology was developed using a modified version of the Simplified Agile Methodology for Ontology Development (SAMOD), which facilitates iterative refinement of classes and relationships, as well as the participation of a domain expert. The modular development of ApplE combines Ethics Theory with Event Context to capture semantic relationships between ethical principles, agents, actions, consequences, intentions, and domains. Using ApplE, we modeled a use case from the medical domain to demonstrate the ontology's representational expressivity and reasoning capabilities. In addition to ontological reasoning and consistency checks, ApplE is also evaluated using the three-fold testing process of SAMOD. ApplE follows the FAIR principles and is positioned to be used as a reusable semantic and conceptual modeling resource for ethical AI systems and ontology-driven applications.