将混合规则与本体融合技术用于航空领域，提升知识表达与推理效率。
Hybrid MKNF for Aeronautics Applications: Usage and Heuristics
- 融合规则与本体的混合MKNF框架实现复杂知识建模。
- 提出适配航空应用的增强表达特性与集成启发式方法。
- 适用于需高精度知识推理的航空航天系统开发场景。
在航空应用中部署知识表示与推理技术面临两大挑战：充分表达复杂领域知识，同时高效执行推理任务，以最小化内存占用和计算开销。一种有效提升表达能力的策略是整合规则与本体两种基础知识表示概念。本研究采用成熟的混合MKNF（Hybrid MKNF）知识表示语言，因其在语义和查询回答能力上实现了规则与本体的无缝融合。通过一个具体案例研究，评估了混合MKNF在航空领域的适用性。研究识别出对航空应用至关重要的额外表达能力特征，并提出了若干启发式方法，以支持这些特征融入混合MKNF框架。
原文摘要 · Abstract (English)
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.
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