Research Area:  Machine Learning

Abstract:

Information resources have increased rapidly in the big data era. Geospatial data plays an indispensable role in spatially informed analyses, while data in different areas are relatively isolated. Therefore, it is inadequate to use relational data in handling many semantic intricacies and retrieving geospatial data. In light of this, a heterogeneous retrieval method based on knowledge graph is proposed in this paper. There are three advantages of this method: (1) the semantic knowledge of geospatial data is considered; (2) more information required by users could be obtained; (3) data retrieval speed can be improved. Firstly, implicit semantic knowledge is studied and applied to construct a knowledge graph, integrating semantics in multi-source heterogeneous geospatial data. Then, the query expansion rules and the mappings between knowledge and database are designed to construct retrieval statements and obtain related spatial entities. Finally, the effectiveness and efficiency are verified through comparative analysis and practices. The experiment indicates that the method could automatically construct database retrieval statements and retrieve more relevant data. Additionally, users could reduce the dependence on data storage mode and database Structured Query Language syntax. This paper would facilitate the sharing and outreach of geospatial knowledge for various spatial studies.

Keywords:  

Author(s) Name:  Junnan Liu ,Haiyan Liu ,Xiaohui Chen ,Xuan Guo ,Qingbo Zhao,Jia Li ,Lei Kang 1 and Jianxiang Liu

Publisher name:  MDPI

DOI:  10.3390/su13042005

Volume Information:  Volume 13 Issue 4

Paper Link:   https://www.mdpi.com/2071-1050/13/4/2005