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ISPRS Int. J. Geo-Inf. 2018, 7(3), 90; https://doi.org/10.3390/ijgi7030090

Similarity Measurement of Metadata of Geospatial Data: An Artificial Neural Network Approach

1,2,3
,
1,2
and
1,2,4,*
1
State Key Laboratory of Resources and Environmental Information System, Beijing 100101, China
2
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
3
University of Chinese Academy of Sciences, Beijing 100049, China
4
Jiang Su Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
*
Author to whom correspondence should be addressed.
Received: 24 December 2017 / Revised: 25 February 2018 / Accepted: 7 March 2018 / Published: 9 March 2018
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Abstract

To help users discover the most relevant spatial datasets in the ever-growing global spatial data infrastructures (SDIs), a number of similarity measures of geospatial data based on metadata have been proposed. Researchers have assessed the similarity of geospatial data according to one or more characteristics of the geospatial data. They created different similarity algorithms for each of the selected characteristics and then combined these elementary similarities to the overall similarity of the geospatial data. The existing combination methods are mainly linear and may not be the most accurate. This paper reports our experiences in attempting to learn the optimal non-linear similarity integration functions, from the knowledge of experts, using an artificial neural network. First, a multiple-layer feed forward neural network (MLFFN) was created. Then, the intrinsic characteristics were used to represent the metadata of geospatial data and the similarity algorithms for each of the intrinsic characteristics were built. The training and evaluation data of MLFFN were derived from the knowledge of domain experts. Finally, the MLFFN was trained, evaluated, and compared with traditional linear combination methods, which was mainly a weighted sum. The results show that our method outperformed the existing methods in terms of precision. Moreover, we found that the combination of elementary similarities of experts to the overall similarity of geospatial data was not linear. View Full-Text
Keywords: artificial neural networks; geospatial data; similarity; metadata; intrinsic characteristics; combination artificial neural networks; geospatial data; similarity; metadata; intrinsic characteristics; combination
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Chen, Z.; Song, J.; Yang, Y. Similarity Measurement of Metadata of Geospatial Data: An Artificial Neural Network Approach. ISPRS Int. J. Geo-Inf. 2018, 7, 90.

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