research-article

Improving geo-spatial linked data with the wisdom of the crowds

Authors:

Michele MelchioriAuthors Info & Claims

EDBT '13: Proceedings of the Joint EDBT/ICDT 2013 Workshops

Pages 68 - 74

https://doi.org/10.1145/2457317.2457329

Published: 18 March 2013 Publication History

Abstract

Currently, there is more and more interest in geo-spatial data sources providing rich information about a huge number of interconnected geo-entities and points of interest located in the real world. Moreover, this kind of sources is one of the first to be published as linked open data. Noteworthy examples are the Geonames and GeoLinkedData initiatives. On the one hand, making available more data sources as linked open data allows querying the sources in an integrated way. On the other hand, it is known that content of geo-spatial data sources suffers from various drawbacks, mainly concerning data quality and conflicts. In this context, relevant feedbacks from users with specific experience and knowledge about POIs in a certain spatial region are considered valuable contributions to improve data quality and solve description conflicts. In this context, we propose a conceptual framework called M-PREGeD (Multi-Providers cRowd-Enhanced Geo linked Data) devoted to collect, organize and rank user-generated corrections and completions to improve accuracy and completeness of Geo-spatial Linked Data from different data sources. Metrics have been defined for both contributors and contents. In the framework, validated and ranked corrections and completions are stored as linked open data in a separate repository but linked to the original data sources. The repository can be queried in a combined way with the original data sources.

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Cited By

Yaman BThompson KFahey FBrennan R(2024)LinkedDataOps:quality oriented end-to-end geospatial linked data production governanceSemantic Web10.3233/SW-23329315:2(555-581)Online publication date: 30-Apr-2024
https://doi.org/10.3233/SW-233293
Kamali MMalek MSaeedi SLiang S(2021)A Blockchain-Based Spatial Crowdsourcing System for Spatial Information Collection Using a Reward DistributionSensors10.3390/s2115514621:15(5146)Online publication date: 29-Jul-2021
https://doi.org/10.3390/s21155146
Wang TXie XCao XPedersen TWang YXiao M(2021)On Efficient and Scalable Time-Continuous Spatial Crowdsourcing2021 IEEE 37th International Conference on Data Engineering (ICDE)10.1109/ICDE51399.2021.00109(1212-1223)Online publication date: Apr-2021
https://doi.org/10.1109/ICDE51399.2021.00109
Show More Cited By

Index Terms

Improving geo-spatial linked data with the wisdom of the crowds
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing systems and tools
  2. Visualization
    1. Visualization application domains
      1. Geographic visualization
2. Information systems
  1. Information systems applications
    1. Spatial-temporal systems
  2. World Wide Web

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cover image ACM Other conferences

EDBT '13: Proceedings of the Joint EDBT/ICDT 2013 Workshops

March 2013

423 pages

ISBN:9781450315999

DOI:10.1145/2457317

General Chair:
Giovanna Guerrini
Università di Genova, Italy

Copyright © 2013 ACM.

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Association for Computing Machinery

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Publication History

Published: 18 March 2013

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EDBT/ICDT '13

EDBT/ICDT '13: Joint 2013 EDBT/ICDT Conferences

March 18 - 22, 2013

Genoa, Italy

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EDBT '13 Paper Acceptance Rate 7 of 10 submissions, 70%;

Overall Acceptance Rate 7 of 10 submissions, 70%

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Cited By

Yaman BThompson KFahey FBrennan R(2024)LinkedDataOps:quality oriented end-to-end geospatial linked data production governanceSemantic Web10.3233/SW-23329315:2(555-581)Online publication date: 30-Apr-2024
https://doi.org/10.3233/SW-233293
Kamali MMalek MSaeedi SLiang S(2021)A Blockchain-Based Spatial Crowdsourcing System for Spatial Information Collection Using a Reward DistributionSensors10.3390/s2115514621:15(5146)Online publication date: 29-Jul-2021
https://doi.org/10.3390/s21155146
Wang TXie XCao XPedersen TWang YXiao M(2021)On Efficient and Scalable Time-Continuous Spatial Crowdsourcing2021 IEEE 37th International Conference on Data Engineering (ICDE)10.1109/ICDE51399.2021.00109(1212-1223)Online publication date: Apr-2021
https://doi.org/10.1109/ICDE51399.2021.00109
Yaman BThompson KBrennan R(2020)Standards Conformance Metrics for Geospatial Linked DataKnowledge Graphs and Semantic Web10.1007/978-3-030-65384-2_9(113-129)Online publication date: 10-Dec-2020
https://doi.org/10.1007/978-3-030-65384-2_9
Cai CGippel JZhu YSingh A(2019)The power of crowds: Grand challenges in the Asia-Pacific regionAustralian Journal of Management10.1177/0312896219871979(031289621987197)Online publication date: 18-Sep-2019
https://doi.org/10.1177/0312896219871979
Gummidi SXie XPedersen T(2019)A Survey of Spatial CrowdsourcingACM Transactions on Database Systems10.1145/329193344:2(1-46)Online publication date: 15-Mar-2019
https://dl.acm.org/doi/10.1145/3291933
Cao WWu ZWang DLi JWu H(2016)Automatic user identification method across heterogeneous mobility data sources2016 IEEE 32nd International Conference on Data Engineering (ICDE)10.1109/ICDE.2016.7498306(978-989)Online publication date: May-2016
https://doi.org/10.1109/ICDE.2016.7498306
Lodigiani CMelchiori M(2016)A PageRank-based Reputation Model for VGI DataProcedia Computer Science10.1016/j.procs.2016.09.08898:C(566-571)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.1016/j.procs.2016.09.088
van Erp MHensel RCeolin Dvan der Meij M(2014)Georeferencing Animal Specimen DatasetsTransactions in GIS10.1111/tgis.1211019:4(563-581)Online publication date: 14-Nov-2014
https://doi.org/10.1111/tgis.12110
Verstockt SSlavkovikj VDe Potter PVan de Walle R(2014)Collaborative Bike Sensing for Automatic Geographic Enrichment: Geoannotation of road\/terrain type by multimodal bike sensingIEEE Signal Processing Magazine10.1109/MSP.2014.232937931:5(101-111)Online publication date: Sep-2014
https://doi.org/10.1109/MSP.2014.2329379
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