Next Article in Journal
New Derivatives on the Fractal Subset of Real-Line
Next Article in Special Issue
Characterization of Seepage Velocity beneath a Complex Rock Mass Dam Based on Entropy Theory
Previous Article in Journal
Feature Selection of Power Quality Disturbance Signals with an Entropy-Importance-Based Random Forest
Previous Article in Special Issue
Hot Spots and Persistence of Nitrate in Aquifers Across Scales
Article

Entropy-Weighted Instance Matching Between Different Sourcing Points of Interest

by 1,2, 1,*, 1 and 1
1
School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
2
Geo-Spatial Information Science Collaborative Innovation Center of Wuhan University, Luoyu Road 129, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Academic Editors: Benjamin L. Ruddell and Kevin H. Knuth
Entropy 2016, 18(2), 45; https://doi.org/10.3390/e18020045
Received: 13 September 2015 / Revised: 24 November 2015 / Accepted: 21 January 2016 / Published: 28 January 2016
(This article belongs to the Special Issue Applications of Information Theory in the Geosciences)
The crucial problem for integrating geospatial data is finding the corresponding objects (the counterpart) from different sources. Most current studies focus on object matching with individual attributes such as spatial, name, or other attributes, which avoids the difficulty of integrating those attributes, but at the cost of an ineffective matching. In this study, we propose an approach for matching instances by integrating heterogeneous attributes with the allocation of suitable attribute weights via information entropy. First, a normalized similarity formula is developed, which can simplify the calculation of spatial attribute similarity. Second, sound-based and word segmentation-based methods are adopted to eliminate the semantic ambiguity when there is a lack of a normative coding standard in geospatial data to express the name attribute. Third, category mapping is established to address the heterogeneity among different classifications. Finally, to address the non-linear characteristic of attribute similarity, the weights of the attributes are calculated by the entropy of the attributes. Experiments demonstrate that the Entropy-Weighted Approach (EWA) has good performance both in terms of precision and recall for instance matching from different data sets. View Full-Text
Keywords: geospatial data; instance matching (POI matching); entropy; word segmentation; category mapping geospatial data; instance matching (POI matching); entropy; word segmentation; category mapping
Show Figures

Graphical abstract

MDPI and ACS Style

Li, L.; Xing, X.; Xia, H.; Huang, X. Entropy-Weighted Instance Matching Between Different Sourcing Points of Interest. Entropy 2016, 18, 45. https://doi.org/10.3390/e18020045

AMA Style

Li L, Xing X, Xia H, Huang X. Entropy-Weighted Instance Matching Between Different Sourcing Points of Interest. Entropy. 2016; 18(2):45. https://doi.org/10.3390/e18020045

Chicago/Turabian Style

Li, Lin, Xiaoyu Xing, Hui Xia, and Xiaoying Huang. 2016. "Entropy-Weighted Instance Matching Between Different Sourcing Points of Interest" Entropy 18, no. 2: 45. https://doi.org/10.3390/e18020045

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop