Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (6A): 230800150-7.doi: 10.11896/jsjkx.230800150

• Big Data & Data Science • Previous Articles     Next Articles

Ontology-driven Study on Information Structuring of Aeronautical Information Tables

LAI Xin, LI Sining, LIANG Changsheng, ZHANG Hengyan   

  1. Civil Aviation Flight University of China,Guanghan,Sichuan 618307,China
  • Published:2024-06-06
  • About author:LAI Xin,born in 1977,Ph.D,associate professor.Her main research interest is aeronautical information services and management.
    LI Sining,born in 1998,postgraduate.Her main research interest is traffic and transportation.
  • Supported by:
    Natural Science Foundation of Sichuan Province,China(2023NSFSC0903) and Key Program of the Central Universities at the School Level(ZJ2023-003).

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Abstract: The aeronautical information publication(AIP) is the main carrier recommended by ICAO to present aeronautical information of all countries,in which a large amount of aeronautical data and aeronautical operation restriction information exists in the form of table information.In order to achieve intelligent querying of AIP and to facilitate the extraction and utilization of static data within it,it is necessary to perform feature extraction and structural processing on the tabular information within AIP.In this paper,an ontology-driven structured extraction method for aeronautical information tabular data is proposed,taking tabular data in AIP as the research object.Firstly,the ontology framework of aeronautical information is constructed to realize a unified and standardized description of domain knowledge.Secondly,the layout structure of form documents is studied and preprocessed using Document AI,and the feature entity extraction is verified and analyzed using random forest algorithm and conditional random field model(CRF).Experimental results show that the proposed method can effectively extract the feature entities in AIP,and provide reference for the in-depth mining of static data in the field of aeronautical information.

Key words: Aeronautical Information, Ontology, Named entity recognition, Conditional random field model, Random forest, Document AI

CLC Number: 

  • TP391
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