Towards Domain-Specific Knowledge Graph Construction for Flight Control Aided Maintenance

Abstract

Flight control is a key system of modern aircraft. During each flight, pilots use flight control to control the forces of flight and also the aircraft’s direction and attitude. Whether flight control can work properly is closely related to safety such that daily maintenance is an essential task of airlines. Flight control maintenance heavily relies on expert knowledge. To facilitate knowledge achievement, aircraft manufacturers and airlines normally provide structural manuals for consulting. On the other hand, computer-aided maintenance systems are adopted for improving daily maintenance efficiency. However, we find that grass-roots engineers of airlines still inevitably consult unstructured technical manuals from time to time, for example, when meeting an unusual problem or an unfamiliar type of aircraft. Achieving effective knowledge from unstructured data is inefficient and inconvenient. Aiming at the problem, we propose a knowledge-graph-based maintenance prototype system as a complementary solution. The knowledge graph we built is dedicated for unstructured manuals referring to flight control. We first build ontology to represent key concepts and relation types and then perform entity-relation extraction adopting a pipeline paradigm with natural language processing techniques. To fully utilize domain-specific features, we present a hybrid method consisting of dedicated rules and a machine learning model for entity recognition. As for relation extraction, we leverage a two-stage Bi-LSTM (bi-directional long short-term memory networks) based method to improve the extraction precision by solving a sample imbalanced problem. We conduct comprehensive experiments to study the technical feasibility on real manuals from airlines. The average precision of entity recognition reaches 85%, and the average precision of relation extraction comes to 61%. Finally, we design a flight control maintenance prototype system based on the knowledge graph constructed and a graph database Neo4j. The prototype system takes alarm messages represented in natural language as the input and returns maintenance suggestions to serve grass-roots engineers.