With the advent of collaborative Web 2.0, spatial data creation is no more exclusively in the hands of professionals. For example, linked open data (LOD) promotes a new paradigm for online and freely accessible spatial information. Noteworthy initiatives in this direction are Geonames and OpenStreetMap. Moreover, as cities are continuously changing and growing, Points of Interest (POIs) are no more historical and their descriptions have to be updated frequently. One appropriate solution is to encourage participation of voluntary on-site experts to the process of information gathering and updating. In this context, we propose a human-enhanced framework, based on linked data principles and technologies, and devoted to collect, organize and rank user-generated corrections and completions in order to improve the accuracy and completeness of Geo-spatial LOD. Metrics have been defined for both human contributors and contents in order to estimate their reliability. The generated data introduces an additional linked data layer for hosting the revised version of the original datasets.

A Human-enhanced Framework for Assessing Open Geo-spatial Data

MELCHIORI, Michele
2014-01-01

Abstract

With the advent of collaborative Web 2.0, spatial data creation is no more exclusively in the hands of professionals. For example, linked open data (LOD) promotes a new paradigm for online and freely accessible spatial information. Noteworthy initiatives in this direction are Geonames and OpenStreetMap. Moreover, as cities are continuously changing and growing, Points of Interest (POIs) are no more historical and their descriptions have to be updated frequently. One appropriate solution is to encourage participation of voluntary on-site experts to the process of information gathering and updating. In this context, we propose a human-enhanced framework, based on linked data principles and technologies, and devoted to collect, organize and rank user-generated corrections and completions in order to improve the accuracy and completeness of Geo-spatial LOD. Metrics have been defined for both human contributors and contents in order to estimate their reliability. The generated data introduces an additional linked data layer for hosting the revised version of the original datasets.
2014
Proceeedings of the Workshops of the 32nd ER International Conference on Conceptual Modeling (ER 2013)
Ateneo di appartenenza
Jeffrey Parsons, Dickson Chiu
PE6_10 Web and information systems, database systems, information retrieval and digital libraries
Esperti anonimi
Inglese
contributo
7th International Workshop on Semantic and Conceptual Issues in GIS (SeCoGIS 2013)
November 11-13
Hong Kong, China
Internazionale
STAMPA
10
Springer Verlag
Geo-spatial Web; Linked Data; Location-based Applications; Model-driven Approach; Human Computation; Crowdsourcing
Ateneo di appartenenza
none
Karam, R.; Melchiori, Michele
273
info:eu-repo/semantics/conferenceObject
2
4 Contributo in Atti di Convegno (Proceeding)::4.1 Contributo in Atti di convegno
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11379/278504
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