Hybrid MKNF for Aeronautics Applications: Usage and Heuristics

TL;DR

This paper explores the use of Hybrid MKNF, a knowledge representation language combining rules and ontologies, for aeronautics applications, focusing on expressivity, efficiency, and heuristics for integration.

Contribution

It evaluates Hybrid MKNF's suitability for aeronautics, identifies key expressivity features, and proposes heuristics to enhance its application in the domain.

Findings

Hybrid MKNF effectively captures complex aeronautics knowledge.

Heuristics improve integration of expressivity features.

Case study demonstrates practical applicability.

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.