Sciweavers

Share
EDBT
2012
ACM

Sieve: linked data quality assessment and fusion

8 years 2 days ago
Sieve: linked data quality assessment and fusion
The Web of Linked Data grows rapidly and already contains data originating from hundreds of data sources. The quality of data from those sources is very diverse, as values may be out of date, incomplete or incorrect. Moreover, data sources may provide conflicting values for a single real-world object. In order for Linked Data applications to consume data from this global data space in an integrated fashion, a number of challenges have to be overcome. One of these challenges is to rate and to integrate data based on their quality. However, quality is a very subjective matter, and finding a canonic judgement that is suitable for each and every task is not feasible. To simplify the task of consuming high-quality data, we present Sieve, a framework for flexibly expressing quality assessment methods as well as fusion methods. Sieve is integrated into the Linked Data Integration Framework (LDIF), which handles Data Access, Schema Mapping and Identity Resolution, all crucial preliminaries...
Pablo N. Mendes, Hannes Mühleisen, Christian
Added 29 Sep 2012
Updated 29 Sep 2012
Type Journal
Year 2012
Where EDBT
Authors Pablo N. Mendes, Hannes Mühleisen, Christian Bizer
Comments (0)
books