Unstructured Transportation Safety Board Findings Categorization Using the Knowledge Graph Pipeline

In this study, the Transportation Safety Board’s (TSB) Findings data was analyzed to assist Transport Canada Civil Aviation (TCCA) in better informing safety policy decision-making. As the TSB Findings data was unstructured, various methods to categorize and analyze unstructured data were explored in the existing literature. It was found that Knowledge Graphs (KGs), in combination with Deep Learning and Natural Language Processing (NLP) models, such as Neuralcoref and REBEL, were versatile and adaptable to different data needs, which could provide insights into the analysis of the TSB data. This paper first emulated and validated the KG pipeline using the BBC News dataset and then applied the KG pipeline technique to the unstructured TSB Findings Reports data consisting of 4,121 rows, each containing text for an incident or accident. The results showed that the model detected an average of 1.03 entities per row of the data and a total of 5,484 relationships or 1.33 relationships per row. Further, the top-four relationships in the graph database structure obtained from Neo4j accounted for 50% of all relations, though not all relations were found to be valuable. However, a few less-frequent relations were also found to be valuable due to their ability to capture critical components of aviation safety. The results of this data pipeline can be used for further analysis and categorization of TSB’s Findings data to improve aviation safety.