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

Chen, Zugang; Song, Jia; Yang, Yibo

doi:10.3390/ijgi7030090

Cited by 8 publications

(7 citation statements)

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%

“…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%

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Progress and Challenges on Entity Alignment of Geographic Knowledge Bases

Sun

Zhu

Song

2019

IJGI

Self Cite

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Geographic knowledge bases (GKBs) with multiple sources and forms are of obvious heterogeneity, which hinders the integration of geographic knowledge. Entity alignment provides an effective way to find correspondences of entities by measuring the multidimensional similarity between entities from different GKBs, thereby overcoming the semantic gap. Thus, many efforts have been made in this field. This paper initially proposes basic definitions and a general framework for the entity alignment of GKBs. Specifically, the state-of-the-art of algorithms of entity alignment of GKBs is reviewed from the three aspects of similarity metrics, similarity combination, and alignment judgement; the evaluation procedure of alignment results is also summarized. On this basis, eight challenges for future studies are identified. There is a lack of methods to assess the qualities of GKBs. The alignment process should be improved by determining the best composition of heterogeneous features, optimizing alignment algorithms, and incorporating background knowledge. Furthermore, a unified infrastructure, techniques for aligning large-scale GKBs, and deep learning-based alignment techniques should be developed. Meanwhile, the generation of benchmark datasets for the entity alignment of GKBs and the applications of this field need to be investigated. The progress of this field will be accelerated by addressing these challenges.

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Section: Alignment Judgementmentioning

confidence: 99%

Section: Alignment Judgementmentioning

confidence: 99%

Progress and Challenges on Entity Alignment of Geographic Knowledge Bases

Sun

Zhu

Song

2019

IJGI

Self Cite

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“…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%

Spatial search strategies for open government data

Degbelo

Teka

2019

Proceedings of the 13th Workshop on Geographic Information Retrieval

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The increasing availability of open government datasets on the Web calls for ways to enable their efficient access and searching. There is however an overall lack of understanding regarding spatial search strategies which would perform best in this context. To address this gap, this work has assessed the impact of different spatial search strategies on performance and user relevance judgment. We harvested machine-readable spatial datasets and their metadata from three English-based open government data portals, performed metadata enhancement, developed a prototype and performed both a theoretical and user-based evaluation. The results highlight that (i) switching between area of overlap and Hausdorff distance for spatial similarity computation does not have any substantial impact on performance; and (ii) the use of Hausdorff distance induces slightly better user relevance ratings than the use of area of overlap. The data collected and the insights gleaned may serve as a baseline against which future work can compare.

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“…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%

Hazard Assessment of Earthquake Disaster Chains Based on a Bayesian Network Model and ArcGIS

Han

Zhang

et al. 2019

IJGI

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The impacts of earthquakes and secondary disasters on ecosystems and the environment are attracting increasing global attention. Development of uncertainty reasoning models offers a chance to research these complex correlations. The primary aim of this research was to construct a disaster chain hazard assessment model that combines a Bayesian Network model and the ArcGIS program software for Changbai Mountain, China, an active volcano with a spate of reported earthquakes, collapses, and landslide events. Furthermore, the probability obtained by the Bayesian Networks was used to determine the disaster chain probability and hazard intensity of the earthquake events, while ArcGIS was used to produce the disaster chain hazard map. The performance of the Bayesian Network model was measured by error rate and scoring rules. The confirmation of the outcomes of the disaster chain hazard assessment model shows that the model demonstrated good predictive performance on the basis of the area under the curve, which was 0.7929. From visual inspection of the produced earthquake disaster chain hazard map, highly hazardous zones are located within a 15 km radius from the Tianchi center, while the northern and the western parts of the studied area are characterized mainly by “very low” to “low” hazard values.

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

Cited by 8 publications

References 41 publications

Progress and Challenges on Entity Alignment of Geographic Knowledge Bases

Progress and Challenges on Entity Alignment of Geographic Knowledge Bases

Spatial search strategies for open government data

Hazard Assessment of Earthquake Disaster Chains Based on a Bayesian Network Model and ArcGIS

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