Acta Geodaetica et Cartographica Sinica ›› 2022, Vol. 51 ›› Issue (2): 279-289.doi: 10.11947/j.AGCS.2022.20210168

• Cartography and Geoinformation • Previous Articles     Next Articles

Ontology knowledge reasoning method for multi-source intelligent road selection

GUO Xuan1, QIAN Haizhong1, WANG Xiao1, LIU Junnan2, REN Yan1, ZHAO Yuzhe1, CHEN Guoqing1   

  1. 1. Institute of Geospatial Information, Information Engineering University, Zhengzhou 450000, China;
    2. Institute of Data and Target Engineering, Information Engineering University, Zhengzhou 450000, China
  • Received:2021-04-02 Revised:2021-06-08 Published:2022-02-28
  • Supported by:
    The National Natural Science Foundation of China (No. 41571442); The Excellent Youth Foundation of Henan Scientific Committee (No. 212300410014); "Dual" Construction Projects for the Military (No. f4203)

Abstract: In the era of big data, multi-source data is increasing. However, there is a semantic inconsistency among multi-source data, we propose an intelligent road selection method based on ontology knowledge reasoning. In this paper, we use basic scale map as basic case and use navigation data and OSM data as experimental data. Features such as grade, length, degree, closeness and betweenness are calculated based on road stroke, and their concepts are extracted to construct a road selection ontology. In order to correlate basic case with experimental data, conceptual similarity is calculated from semantic feature and numerical feature. Then, ontology and semantic web rule language are used to define road selection rules and reason the process knowledge of cartographic generalization, which realize the automatic selection of multi-source road data. The experiments indicate that our method can effectively eliminate the semantic inconsistency among multi-source data to realize the road intelligent selection in similar areas.

Key words: road selection, multi-source data, ontology, similarity, semantic web rule language

CLC Number: