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

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

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

Content Provider	ACM Digital Library
Author	Melchiori, Michele Karam, Roula
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.
Starting Page	68
Ending Page	74
Page Count	7
File Format	PDF
ISBN	9781450315999
DOI	10.1145/2457317.2457329
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-03-18
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Crowdsourcing Geospatial web Location based applications Human computation Linked data Model-driven approach
Content Type	Text
Resource Type	Article

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