Mapping Spatiotemporal Data to RDF: A SPARQL Endpoint for Brussels

Vaisman, Alejandro; Chentout, Kevin

doi:10.3390/ijgi8080353

Cited by 6 publications

(4 citation statements)

References 16 publications

(10 reference statements)

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“…This line of research has been addressed in different studies. Vaisman et al [34] studied the problem of capturing spatio-temporal data from different data sources, integrating these data and storing them in a geospatial RDF data store. Eventually, these data were enriched with external data from LinkedGeoData [5], GeoNames [36], and DBpedia [4].…”

Section: Related Workmentioning

confidence: 99%

“…However, this line of work does not address the issue of entity interlinking that is essential for building linked geospatial data for the task of change analysis with a semantic relationship between geospatial entities across map editions of the same region, which could be part of a large collection of topographic map sheets. Similar work is concerned with only a specific type of geometry, such as points, as in [2,34], or is limited to a specific physical feature (i.e. flooded areas [17] or wildfires [18]).…”

Section: Introductionmentioning

confidence: 99%

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Building spatio-temporal knowledge graphs from vectorized topographic historical maps

Shbita

Knoblock

Duan

et al. 2023

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Historical maps provide rich information for researchers in many areas, including the social and natural sciences. These maps contain detailed documentation of a wide variety of natural and human-made features and their changes over time, such as changes in transportation networks or the decline of wetlands or forest areas. Analyzing changes over time in such maps can be labor-intensive for a scientist, even after the geographic features have been digitized and converted to a vector format. Knowledge Graphs (KGs) are the appropriate representations to store and link such data and support semantic and temporal querying to facilitate change analysis. KGs combine expressivity, interoperability, and standardization in the Semantic Web stack, thus providing a strong foundation for querying and analysis. In this paper, we present an automatic approach to convert vector geographic features extracted from multiple historical maps into contextualized spatio-temporal KGs. The resulting graphs can be easily queried and visualized to understand the changes in different regions over time. We evaluate our technique on railroad networks and wetland areas extracted from the United States Geological Survey (USGS) historical topographic maps for several regions over multiple map sheets and editions. We also demonstrate how the automatically constructed linked data (i.e., KGs) enable effective querying and visualization of changes over different points in time.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Building spatio-temporal knowledge graphs from vectorized topographic historical maps

Shbita

Knoblock

Duan

et al. 2023

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“…At present, spatiotemporal data are usually provided as relational tables [27] or XML documents [4], which can be mapped into the RDF data model using R2RML [46]. R2RML is a standard language that allows defining customized mappings from relational databases to RDF datasets.…”

Section: Spatiotemporal Data Model On Rdfmentioning

confidence: 99%

“…R2RML is a standard language that allows defining customized mappings from relational databases to RDF datasets. In [46], data are spatiotemporal in nature and R2RML can produce spatiotemporal Linked Open Data. Data generated in this way are used to populate a SPARQL endpoint.…”

Section: Spatiotemporal Data Model On Rdfmentioning

confidence: 99%

stRDFS: Spatiotemporal Knowledge Graph Modeling

Zhu

Bai

et al. 2020

IEEE Access

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In Semantic Web, modeling knowledge graph based on RDF becomes more and more popular. There is quite a lot of spatiotemporal information in Semantic Web, and recent works focus on not only general data but also spatiotemporal data. Existing efforts are mainly to add spatiotemporal labels to RDF, which expand RDF triple into quad or quintuple. However, extra labels often cause additional overhead for the system and lead to inefficient information organization management. In order to overcome this limitation, we propose an stRDFS model by labeling properties with spatiotemporal features and the corresponding determination methods of topological relations among different spatiotemporal entities. stRDFS considers spatiotemporal attribute as a part of the RDF model, which can record spatiotemporal information without changing the current RDF standard. Our approach improves the ability of recording and linking spatiotemporal data. More importantly, depending on formatting of spatiotemporal attributes in stRDFS, it will improve the semantic inferring ability, and the users are not required to be familiar with the underlying representations of spatiotemporal data.

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