# 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. Flight control system maintenance is a task heavily relying on expert experiences that are usually accumulated and recorded in documents. For example, from the perspective of the manufacturer of aircraft carriers, manuals such as a TSM (trouble-shooting manual) are provided to guide daily maintenance and fault diagnosis. On the other hand, airline companies also keep massive logs of fault diagnosis for future maintenance and staff training. According to our investigation, a TSM and logs of fault diagnosis are the basis of daily maintenance. A common feature of them is that they are structural and can be easily stored in a relational database. When meeting a trouble code, grass-roots engineers are capable of consulting the database to obtain effective information for fault localization. As new aircraft are introduced, the complexity of flight control system grows. It is more and more usual that grass-roots engineers cannot quickly diagnose a problem based only on structural data. To aid engineers, computer-assisted diagnostic systems have appeared [1; 2; 3; 4; 5; 6]. These systems rely on building a fault diagnosis model either by expert knowledge or massive historical records. However, when confronted with a new aircraft type or unusual problems, model-based methods are often inadequate, and engineers need to consult a set of unstructured data such as technical training manuals, flight control system instructions, etc.to localize the faults. Consulting unstructured data is inefficient and inconvenient. Aiming at the problem, we would like to study a knowledge-graph-based method, where effective information is first extracted to build a semantic knowledge base, and then an aided maintenance prototype system is built based on the knowledge extracted. A knowledge graph built on open-source information, e.g., Wikipedia, is common.