Hybrid MKNF for Aeronautics Applications: Usage and Heuristics
Arun Raveendran Nair Sheela, Florence De Grancey, Christophe Rey, Victor Charpenay
TL;DR
The paper addresses expressivity and efficiency challenges in aeronautics KRR by adopting Hybrid MKNF to unite Description Logic ontologies with Logic Programs, enabling robust query answering under resource constraints. It emphasizes well-founded semantics for efficiency, introduces the Notam-Aware Reasoning System (NARS) as a concrete use case, and provides an implementation pathway via the NoHr reasoner with offline preprocessing to fit on limited hardware. Core contributions include a heuristic integration of classical negation and integrity constraints, a transformation tool to convert Hybrid MKNF KBs into Prolog-compatible rule programs, and an evaluation framework outlining preprocessing, latency, and memory implications. The work offers practical guidance for deploying explainable, consistent, and scalable KRR in aviation settings and sketches future directions in formal semantics, provenance, and temporal reasoning for time-sensitive NOTAM data.
Abstract
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.