# Towards Domain-Specific Knowledge Graph Construction for Flight Control Aided Maintenance
###### Abstract
A flight control system is domain-specific such that existing open-source knowledge graphs are not adaptive. Until now, only a few studies on knowledge graphs have examined flight control [7; 8]. However, they mainly focused on knowledge referring to faults (e.g., TSM) but did not draw enough attention to knowledge in other important manuals written with unstructured text. Combining with the current flight control system maintenance, we suggest that a system that can quickly provide knowledge from unstructured manuals is still missing. Hence, we will design a knowledge-graph-based system to fix the gap. Building a knowledge graph in a new area is systematic and consists of both manual and automation work. The system overview is depicted in Figure 1. The target data are unstructured manuals referring to flight control. After format conversion and denoising, we analyze the manuals to understand the semantics and build ontology to represent key concepts and relation types. Next, we label named entities and relations in each sentence in order to perform automatic knowledge extraction by natural language processing techniques. In our case, knowledge extraction is conducted by two sequential tasks: NER (named entity recognition) and RE (relation extraction). For named entity recognition, we present a hybrid method that combines a rule-based-algorithm and a machine-learning-based algorithm. Note that entity recognition is usually difficult to be performed by defining rules. Fortunately, flight control systems are domain-specific, and we found that some entities always appear regularly which gives us a good opportunity to recognize them by defining specific rules. For most entities, we use a machine-learning-based algorithm consisting of a Transformer encoder and a CRF (conditional random field) layer to perform recognition. In addition, to leverage features of flight control systems, we not only use the model word2 vec but also POS (part of speech), domain-specific phrases, and high-frequency word information to obtain the embedding of each token. As for the relation extraction, we perform a two-stage Bi-LSTM (bi-directional long short-term memory networks) based method to relieve the sample imbalanced problem. During the first stage, a binary classification is settled that determines whether there exists a relation between a given pair of entities. After that, the second stage is carried out to differentiate the relation type. Based on the entities and relations extracted, a set of triples are achieved, and then a knowledge graph is constructed. Finally, a knowledge-graph-based prototype system is implemented on Neo4j to return possible fault locations. The main contributions of this paper are summarized in the following:  
1. We construct a knowledge graph via natural language processing techniques aiming at extracting effective knowledge from unstructured manuals referring to flight control systems. 2. To facilitate daily maintenance of flight control, an aided maintenance prototype system is implemented based on the knowledge graph we constructed. The system takes fault alarms as inputs and outputs suggestions for faults localization. 3. We set up comprehensive experiments to verify the proposed methodology. The precision of entity recognition and relation extraction, respectively, comes to 0.85 and 0.61, ensuring the availability of our prototype system. The rest of this paper is organized as follows. Section 2 sketches out the related work, and Section 3 briefly introduces data pre-processing. In Section 4, we introduce the process of knowledge graph construction for flight control from ontology building to entity-relation extraction. Section 5 sets up experiments to evaluate the performance of entity recognition and relation extraction. Ablation experiments are also conducted to evaluate the effectiveness of domain-specific information used for token embedding. Section 6 briefly introduces a maintenance prototype built upon our knowledge graph. Finally, Section 7 brings concluding remarks and future work.