Data di Pubblicazione:
2014
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.
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.
Tipologia CRIS:
4.1 Contributo in Atti di convegno
Keywords:
Geo-spatial Web; Linked Data; Location-based Applications; Model-driven Approach; Human Computation; Crowdsourcing
Elenco autori:
Karam, R.; Melchiori, Michele
Link alla scheda completa:
Titolo del libro:
Proceeedings of the Workshops of the 32nd ER International Conference on Conceptual Modeling (ER 2013)
Pubblicato in: