Semantic-Based Geospatial Data Integration With Unique Features

Semantic-Based Geospatial Data Integration With Unique Features

Ying Zhang, Chaopeng Li, Na Chen, Shaowen Liu, Liming Du, Zhuxiao Wang, Miaomiao Ma
Copyright: © 2019 |Pages: 24
DOI: 10.4018/978-1-5225-8054-6.ch012
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Abstract

Since large amount of geospatial data are produced by various sources, geospatial data integration is difficult because of the shortage of semantics. Despite standardised data format and data access protocols, such as Web Feature Service (WFS), can enable end-users with access to heterogeneous data stored in different formats from various sources, it is still time-consuming and ineffective due to the lack of semantics. To solve this problem, a prototype to implement the geospatial data integration is proposed by addressing the following four problems, i.e., geospatial data retrieving, modeling, linking and integrating. We mainly adopt four kinds of geospatial data sources to evaluate the performance of the proposed approach. The experimental results illustrate that the proposed linking method can get high performance in generating the matched candidate record pairs in terms of Reduction Ratio(RR), Pairs Completeness(PC), Pairs Quality(PQ) and F-score. The integrating results denote that each data source can get much Complementary Completeness(CC) and Increased Completeness(IC).
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1. Introduction

Geospatial data integration can be used to improve data quality, to reduce costs, and to make data more useful to the public(Auer et al.,2009; Bittner et al.,2009; Brodt et al.,2010; Kuhn,2002; Su et al.,2012; De Carvalho et al.,2012; Su & Lochovsky,2010; Ballatore et al.,2014; Buccella et al.,2010; Fonseca, Egenhofer et al.,2002; Malik et al.,2010;Vaccari et al.,2009). However, the large amount of data is produced by a variety of sources, stored in incompatible formats, and accessible through different GIS applications. Thus, geospatial data integration is difficult and becoming an increasingly important subject.

To implement the geospatial data integration, four problems need to be addressed: geospatial data retrieving, modeling, linking and integrating This paper proposes corresponding approach for each issue. Besides, our work takes advantage of Karma (Szekely et al.,2011; Knoblock et al.,2012; Taheriyan et al.,2012; Tuchinda et al.,2011; Knoblock et al.,2011), which is a general information integration tool. It supports importing data from a variety of sources including relational databases, spreadsheet, KML and semi-structured Web pages, and publishing data in a variety of formats such as RDF. The source modeling work is based on these functions.

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