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

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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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
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 18 March 2013

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Author Tags

  1. crowdsourcing
  2. geospatial web
  3. human computation
  4. linked data
  5. location based applications
  6. model-driven approach

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EDBT/ICDT '13

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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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  • (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
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