A Heterogeneous Geospatial Data Retrieval Method Using Knowledge Graph

Junnan Liu,Jia Li,Lei Kang,Xiaohui Chen,Haiyan Liu,Xuan Guo,Jianxiang Liu,Qingbo Zhao

doi:10.3390/su13042005

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.

Highlights

With the advent of the big data era, there has been a significant increase in the amount of multi-source heterogeneous data in various fields, of which more than 50% are relevant to geospatial information [1,2,3]
Ontology-based data access (OBDA) is a popular paradigm for accessing data, it is usually based on a commonsense knowledge base, lacking geospatial semantic knowledge [12,13]
The total number of retrieved entities where R is the ratio of the number of retrieved relevant entities to the total number of relevant entities in original data

Summary

Introduction

With the advent of the big data era, there has been a significant increase in the amount of multi-source heterogeneous data in various fields, of which more than 50% are relevant to geospatial information [1,2,3]. The types of geographic entities can be distinguished with the use of semantics contained in specialized terms and names [4,5]. Existing methods seldom consider the spatial and semantic characteristics comprehensively, leaving great room for improving geospatial data retrieval speed. The multi-source geospatial data is heterogeneous and cannot be retrieved directly [8,9]. It is difficult to consider implicit semantic knowledge and complex spatial relationships, leading to information disorientation and information overload [10,11]. It is urgent to introduce a knowledge graph (KG) to consider

Methods

Results

Conclusion