Assessing the Quality of Spatio-Textual Datasets in the Absence of Ground Truth

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 767)


The increasing availability of enriched geospatial data has opened up a new domain and enables the development of more sophisticated location-based services and applications. However, this development has also given rise to various data quality problems as it is very hard to verify the data for all real-world entities contained in a dataset. In this paper, we propose ARCI, a relative quality indicator which exploits the vast availability of spatio-textual datasets, to indicate how confident a user can be in the correctness of a given dataset. ARCI operates in the absence of ground truth and aims at computing the relative quality of an input dataset by cross-referencing its entries among various similar datasets. We also present an algorithm for computing ARCI and we evaluate its performance in a preliminary experimental evaluation using real-world datasets.


Spatio-textual data Data quality Relative quality 


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© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Masaryk UniversityBrnoCzech Republic
  2. 2.Free University of Bozen-BolzanoSouth TyrolItaly

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