2018
DOI: 10.3390/ijgi7030090
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Similarity Measurement of Metadata of Geospatial Data: An Artificial Neural Network Approach

Abstract: 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 geo… Show more

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Cited by 8 publications
(7 citation statements)
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References 41 publications
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“…Martins also used machine learning techniques to detect duplicate gazetteers [43,44]. Li et al and Chen et al used an artificial neural network (ANN) model to match geographic concepts [28,100]. The supervised machine learning method actually performs a nonlinear combination for multiple similarity metrics [100].…”
Section: Alignment Judgementmentioning
confidence: 99%
See 1 more Smart Citation
“…Martins also used machine learning techniques to detect duplicate gazetteers [43,44]. Li et al and Chen et al used an artificial neural network (ANN) model to match geographic concepts [28,100]. The supervised machine learning method actually performs a nonlinear combination for multiple similarity metrics [100].…”
Section: Alignment Judgementmentioning
confidence: 99%
“…Li et al and Chen et al used an artificial neural network (ANN) model to match geographic concepts [28,100]. The supervised machine learning method actually performs a nonlinear combination for multiple similarity metrics [100]. Although it can learn an optimal scheme for similarity combination automatically, it requires large-scale training datasets, which are difficult to prepare, for a satisfactory trained model.…”
Section: Alignment Judgementmentioning
confidence: 99%
“…These directions were also considered in our systematic comparisons. In another work dealing with geographic information retrieval and ranking in spatial data infrastructures, Chen et al [10] proposed using artificial neural networks to learn from knowledge of experts to integrate the characteristics of geospatial data to the computation of an overall similarity score. Among the similarities integrated, one is thematic similarity in which they used WordNet similarity methods.…”
Section: Linked Open Government Data (Logd)mentioning
confidence: 99%
“…Their work only considers spatial search without due consideration to thematic queries that we incorporated in this study. Another set of works [10,28,34] was mainly about spatial data infrastructures or geospatial catalogs based on metadata, but is of interest for our study.…”
Section: Open Government Datamentioning
confidence: 99%
“…In contrast to the research methods presented above, earthquake disaster chain probability has hardly attracted any research efforts. There are many existing methods of probability reasoning, fuzzy logic, neural networking, expert systems and so on [23][24][25], but the existing models cannot adequately describe the earthquake disaster chain structures and probability. Bayesian Networks (BNs) can address this shortcoming owing to their capability to combine the probabilistic methodology with clear diagrams that encrypt the causality between variables.…”
Section: Introductionmentioning
confidence: 99%