A Spatio-Temporal Resource Description Framework Schema Model for Aeronautical Dynamic Information Based on Semantic Analysis

Introduction

Aeronautical information service (AIS) provides aeronautical data and information for civil air transportation and accounts for a central service module air traffic management. According to requirements proposed by International Civil Aviation Organization for the development of AIS, the aeronautical information will complete the transition from AIS to aeronautical information management (AIM) in the next decade. During the transition phase, large amounts of data on aviation operations will be accumulated, and the correlation between these dynamic data will be mined to extract data features and lay a foundation for subsequent event pattern recognition and event prediction. Due to the massive scale of aeronautical dynamic data, advanced technologies must be relied to fulfill data mining and feature extraction. Knowledge graph (KG), a technique that describes concepts, entities and associations in a structured form, can be employed to organize and manage massive information and represent information in a more cognitive form for humans. KG has already been widely adopted in finance, medicine, and e-commerce and other industries, such as the semantic search function of Ali, Baidu, British Museum, etc. Theories and applications of KG have revealed that it is a useful tool to build high-quality and integrated information databases and mine data relevance. The key is to use a canonical model framework to integrate and cluster aeronautical dynamic information. The commonly used description method for semantic representation is the resource description framework, the first step of KG.

In this work, we performed semantic analysis on the aeronautical dynamic information of NOTAMs in AIS. By combining semantic analysis and a triple RDF model, a spatio-temporal RDF model was established to extract features of aeronautical information. A new model was constructed using Protégé, and the sample NOTAMs texts were employed to verify the constructed model. The model can effectively match the sample NOTAM information and extract features from within. The constructed model can lay a foundation for subsequent mining of aeronautical information based on KG.