Using Provenance for Quality Assessment and Repair in Linked Open Data

As the number of data sources publishing their data on the Web of Data is growing, we are experiencing an immense growth of the Linked Open Data cloud. The lack of control on the published sources, which could be untrustworthy or unreliable, along with their dynamic nature that often invalidates links and causes conflicts or other discrepancies, could lead to poor quality data. In order to judge data quality, a number of quality indicators have been proposed, coupled with quality metrics that quantify the “quality level” of a dataset. In addition to the above, some approaches address how to improve the quality of the datasets through a repair process that focuses on how to correct invalidities caused by constraint violations by either removing or adding triples. In this paper we argue that provenance is a critical factor that should be taken into account during repairs to ensure that the most reliable data is kept. Based on this idea, we propose quality metrics that take into account provenance and evaluate their applicability as repair guidelines in a particular data fusion setting.

Repair works at Saint Claire Convent in Chinchón (Madrid, Spain)

The Chinchón Saint Claire Convent is a Count Foundation of 1653. Some repair works were made around 1965, but some important cracks were remained. This paper describes the soil study made to know, its properties, the reason of these movements and its consequences to the convent and the repair works done. A high swelling phenomenon has been detected. The convent is on a high plasticity clayey soil with soil sandy insertions. The soil water has a high content of sulphates. In addition, some sewer system piping was broken around the convent and frequently flooded the convent crypt. A micropiles underpinning was made and the water leaked has lead to the sewer system to avoid the crypt flooding, also a drainage system has been made and the ground around the church has been paved.