Semantic integration of <scp>OpenStreetMap</scp> and <scp>CityGML</scp> with formal concept analysis

Ahmadian, Somayeh; Pahlavani, Parham

doi:10.1111/tgis.13006

Cited by 5 publications

(2 citation statements)

References 58 publications

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“…In the context of the Semantic Web, Codescu et al (2011) developed an ontology for OSM tags in English, while Ahmadian and Pahlavani (2022) evaluated the heterogeneity in the descriptions of the OSM tags and proposed a formal concept analysis to perform the integration of the building categories with the corresponding CityGML classification. Finally, Neumaier et al (2018) integrated and linked datasets to create a base knowledge graph of geo-entities, adding semantic labels and disambiguating entities using context and hierarchy.…”

Section: Background and Related Workmentioning

confidence: 99%

Semantic Alignment of Official and Collaborative Geospatial Data: A Case Study in Brazil

Machado,

Camboim

2024

Rev. Bras. Cartogr.

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Geospatial data is crucial for sustainable development, but obtaining up-to-date and high-quality data is challenging in many regions, including Brazil. Collaborative mapping on platforms such as OpenStreetMap (OSM) has produced updated and open geospatial data, especially in urban areas, but its quality is heterogeneous. In addition, semantic interoperability is challenging when integrating OSM data with authoritative geospatial data. This article presents a procedure for semantic alignment between two conceptual models within a conflation process to elicit background knowledge for geospatial data integration. The first model is the Technical Specification for Structuring Vector Geospatial Data (ET-EDGV 3.0) in Brazilian Portuguese, and the second is the OSM model with tags mainly in English. The alignment produced a table combining the ET-EDGV classes, attributes, domains, and geometries with the OSM tags and elements. The semantic alignment was tested in two study areas to check the thematic accuracy of transportation data imported from OSM compared to the data in the reference database. The study found that the best percentage of segments correctly classified by alignment was for "highway=trunk" tags (98.27%) and "highway=primary" (98.20%), corresponding to road and highway segments, and for the "highway=residential" tag (76.20%), corresponding to sections of residential streets. The study also identified factors that may contribute to low accuracy rates, including ambiguous semantic descriptions and the need for local context analysis. This research contributes to adding collaborative data to the official mapping, a relevant alternative for updating and supplementing reference mapping that can be applied in other geographical contexts.

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Section: Background and Related Workmentioning

confidence: 99%

Semantic Alignment of Official and Collaborative Geospatial Data: A Case Study in Brazil

Machado,

Camboim

2024

Rev. Bras. Cartogr.

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“…Therefore, it is of great theoretical and practical importance to study the attribute reduction method of the concept lattice. This method can provide powerful tools for knowledge discovery [7][8][9], data mining [10][11][12], information retrieval [13], semantic networks [14,15], and ontology construction [16,17] based on concept lattice theory.…”

Section: Introductionmentioning

confidence: 99%

Stream Convolution for Attribute Reduction of Concept Lattices

Xu,

Wu,

et al. 2023

Mathematics

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Attribute reduction is a crucial research area within concept lattices. However, the existing works are mostly limited to either increment or decrement algorithms, rather than considering both. Therefore, dealing with large-scale streaming attributes in both cases may be inefficient. Convolution calculation in deep learning involves a dynamic data processing method in the form of sliding windows. Inspired by this, we adopt slide-in and slide-out windows in convolution calculation to update attribute reduction. Specifically, we study the attribute changing mechanism in the sliding window mode of convolution and investigate five attribute variation cases. These cases consider the respective intersection of slide-in and slide-out attributes, i.e., equal to, disjoint with, partially joint with, containing, and contained by. Then, we propose an updated solution of the reduction set for simultaneous sliding in and out of attributes. Meanwhile, we propose the CLARA-DC algorithm, which aims to solve the problem of inefficient attribute reduction for large-scale streaming data. Finally, through the experimental comparison on four UCI datasets, CLARA-DC achieves higher efficiency and scalability in dealing with large-scale datasets. It can adapt to varying types and sizes of datasets, boosting efficiency by an average of 25%.

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