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Towards knowledge-based integration and visualization of geospatial data using semantic web technologies*

Huang, Weiming LU (2018) 2018 Doctoral Consortium and Challenge at RuleML+RR, RuleML+RR-DCC 2018 In CEUR Workshop Proceedings 2204.
Abstract

Geospatial data have been pervasive and indispensable for various real-world application of e.g. urban planning, traffic analysis and emergency response. To this end, the data integration and knowledge transfer are two prominent issues for augmenting the use of geospatial data and knowledge. In order to address these issue, Semantic Web technologies have been considerably adopted in geospatial domain, and there are currently still some activates investigating the benefits brought up from the adoption of Semantic Web technologies. In this context, this paper showcases and discusses the knowledge-based geospatial data integration and visualization leveraging ontologies and rules. Specifically, we use the Linked Data paradigm for modelling... (More)

Geospatial data have been pervasive and indispensable for various real-world application of e.g. urban planning, traffic analysis and emergency response. To this end, the data integration and knowledge transfer are two prominent issues for augmenting the use of geospatial data and knowledge. In order to address these issue, Semantic Web technologies have been considerably adopted in geospatial domain, and there are currently still some activates investigating the benefits brought up from the adoption of Semantic Web technologies. In this context, this paper showcases and discusses the knowledge-based geospatial data integration and visualization leveraging ontologies and rules. Specifically, we use the Linked Data paradigm for modelling geospatial data, and then create knowledge base of the visualization of such data in terms of scaling, data portrayal and geometry source. This approach would benefit the transfer, interpret and reuse the visualization knowledge for geospatial data. At the meantime, we also identified some challenges of modelling geospatial knowledge and outreaching such knowledge to other domains as future study.

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Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Data integration, Data visualization, Geospatial data, Ontologies, Rule-based inference, Semantic Web
host publication
Proceedings of the Doctoral Consortium and Challenge @ RuleML+RR 2018 hosted by 2nd International Joint Conference on Rules and Reasoning (RuleML+RR 2018)
series title
CEUR Workshop Proceedings
volume
2204
conference name
2018 Doctoral Consortium and Challenge at RuleML+RR, RuleML+RR-DCC 2018
conference location
Luxembourg, Luxembourg
conference dates
2018-09-20 - 2018-09-26
external identifiers
  • scopus:85053760210
ISSN
1613-0073
language
English
LU publication?
yes
id
0d8eb4eb-79eb-4c6e-9388-2454390ae50a
alternative location
http://ceur-ws.org/Vol-2204/paper4.pdf
date added to LUP
2018-10-22 12:15:22
date last changed
2019-01-06 14:11:29
@inproceedings{0d8eb4eb-79eb-4c6e-9388-2454390ae50a,
  abstract     = {<p>Geospatial data have been pervasive and indispensable for various real-world application of e.g. urban planning, traffic analysis and emergency response. To this end, the data integration and knowledge transfer are two prominent issues for augmenting the use of geospatial data and knowledge. In order to address these issue, Semantic Web technologies have been considerably adopted in geospatial domain, and there are currently still some activates investigating the benefits brought up from the adoption of Semantic Web technologies. In this context, this paper showcases and discusses the knowledge-based geospatial data integration and visualization leveraging ontologies and rules. Specifically, we use the Linked Data paradigm for modelling geospatial data, and then create knowledge base of the visualization of such data in terms of scaling, data portrayal and geometry source. This approach would benefit the transfer, interpret and reuse the visualization knowledge for geospatial data. At the meantime, we also identified some challenges of modelling geospatial knowledge and outreaching such knowledge to other domains as future study.</p>},
  author       = {Huang, Weiming},
  booktitle    = {CEUR Workshop Proceedings},
  issn         = {1613-0073},
  keyword      = {Data integration,Data visualization,Geospatial data,Ontologies,Rule-based inference,Semantic Web},
  language     = {eng},
  location     = {Luxembourg, Luxembourg},
  title        = {Towards knowledge-based integration and visualization of geospatial data using semantic web technologies<sup>*</sup>},
  volume       = {2204},
  year         = {2018},
}