The deployment of knowledge representation and reasoning technologies in aeronautics applications presents two main challenges: achieving sufficient expressivity to capture complex domain knowledge, and executing reasoning tasks efficiently while minimizing memory usage and computational overhead. An effective strategy for attaining necessary expressivity involves integrating two fundamental KR concepts: rules and ontologies. This study adopts the well-established KR language Hybrid MKNF owing to its seamless integration of rules and ontologies through its semantics and query answering capabilities. We evaluated Hybrid MKNF to assess its suitability in the aeronautics domain through a concrete case study. We identified additional expressivity features that are crucial for developing aeronautics applications and proposed a set of heuristics to support their integration into Hybrid MKNF framework.

翻译：在航空应用中部署知识表示与推理技术面临两大主要挑战：一是需要足够的表达能力以捕捉复杂的领域知识，二是需要在最小化内存占用和计算开销的同时高效执行推理任务。实现必要表达能力的有效策略涉及整合两个基本的知识表示概念：规则与本体。本研究采用成熟的知识表示语言混合MKNF，因其能通过其语义和查询应答能力无缝整合规则与本体。我们通过具体案例研究评估了混合MKNF，以判断其在航空领域的适用性。我们识别了对开发航空应用至关重要的额外表达能力特征，并提出了一套启发式方法来支持将这些特征整合到混合MKNF框架中。