Knowledge-based geospatial data integration and visualization with Semantic Web technologies
(2019) 2019 Doctoral Consortium at the 18th International Semantic Web Conference, ISWC-DC 2019 In CEUR Workshop Proceedings 2548. p.37-45- Abstract
Geospatial information is indispensable for various spatially-informed analysis and decision-making, e.g. traffic analysis and built environment processes. Geospatial data often must be integrated for meaningful analysis, whereas such integration is challenging due to siloed data organization, semantic heterogeneity and multiple representation of geospatial data. Moreover, the visualization of geospatial data is one of the most prominent ways of utilizing geospatial data, however how to properly visualize the data is sometime difficult, as it pertains to a wide range of visualization (cartographic) knowledge. Semantic Web technologies unveil a promising way to mitigate these issues, as they provide means of data integration on the Web,... (More)
Geospatial information is indispensable for various spatially-informed analysis and decision-making, e.g. traffic analysis and built environment processes. Geospatial data often must be integrated for meaningful analysis, whereas such integration is challenging due to siloed data organization, semantic heterogeneity and multiple representation of geospatial data. Moreover, the visualization of geospatial data is one of the most prominent ways of utilizing geospatial data, however how to properly visualize the data is sometime difficult, as it pertains to a wide range of visualization (cartographic) knowledge. Semantic Web technologies unveil a promising way to mitigate these issues, as they provide means of data integration on the Web, and knowledge representation capacity to formally represent the visualization knowledge. In this PhD project, we investigate the potential values of Semantic Web technologies for geospatial data integration (particularly for geospatial data with multiple representation) and visualization in several cases, where the integration and visualization knowledge is formalized using Semantic Web technologies. All the case studies embody realworld meaning and entail data integration and visualization challenge, which have been addressed by state-of-the-art solutions inadequately. Preliminary results demonstrate great yet not fully unlocked potential of Semantic Web technologies for geospatial data, and also disclose challenges that need to be addressed.
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- author
- Huang, Weiming LU
- organization
- publishing date
- 2019
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Data integration, Data visualization, Geospatial data, Ontologies, Semantic rules, Semantic Web, SHACL, Spatial analysis, Web maps
- host publication
- Proceedings of the Doctoral Consortium at ISWC 2019
- series title
- CEUR Workshop Proceedings
- volume
- 2548
- pages
- 9 pages
- publisher
- CEUR-WS
- conference name
- 2019 Doctoral Consortium at the 18th International Semantic Web Conference, ISWC-DC 2019
- conference location
- Auckland, New Zealand
- conference dates
- 2019-10-27
- external identifiers
-
- scopus:85081630624
- ISSN
- 1613-0073
- language
- English
- LU publication?
- yes
- id
- a75397d7-c1bf-4b82-a2b7-f6e6ea3edd9f
- alternative location
- http://ceur-ws.org/Vol-2548/paper-04.pdf
- date added to LUP
- 2020-03-31 12:37:28
- date last changed
- 2022-04-18 21:29:33
@inproceedings{a75397d7-c1bf-4b82-a2b7-f6e6ea3edd9f,
abstract = {{<p>Geospatial information is indispensable for various spatially-informed analysis and decision-making, e.g. traffic analysis and built environment processes. Geospatial data often must be integrated for meaningful analysis, whereas such integration is challenging due to siloed data organization, semantic heterogeneity and multiple representation of geospatial data. Moreover, the visualization of geospatial data is one of the most prominent ways of utilizing geospatial data, however how to properly visualize the data is sometime difficult, as it pertains to a wide range of visualization (cartographic) knowledge. Semantic Web technologies unveil a promising way to mitigate these issues, as they provide means of data integration on the Web, and knowledge representation capacity to formally represent the visualization knowledge. In this PhD project, we investigate the potential values of Semantic Web technologies for geospatial data integration (particularly for geospatial data with multiple representation) and visualization in several cases, where the integration and visualization knowledge is formalized using Semantic Web technologies. All the case studies embody realworld meaning and entail data integration and visualization challenge, which have been addressed by state-of-the-art solutions inadequately. Preliminary results demonstrate great yet not fully unlocked potential of Semantic Web technologies for geospatial data, and also disclose challenges that need to be addressed.</p>}},
author = {{Huang, Weiming}},
booktitle = {{Proceedings of the Doctoral Consortium at ISWC 2019}},
issn = {{1613-0073}},
keywords = {{Data integration; Data visualization; Geospatial data; Ontologies; Semantic rules; Semantic Web; SHACL; Spatial analysis; Web maps}},
language = {{eng}},
pages = {{37--45}},
publisher = {{CEUR-WS}},
series = {{CEUR Workshop Proceedings}},
title = {{Knowledge-based geospatial data integration and visualization with Semantic Web technologies}},
url = {{http://ceur-ws.org/Vol-2548/paper-04.pdf}},
volume = {{2548}},
year = {{2019}},
}