Linked data cleansing and change management

Abstract

The Web of Data is constantly growing in terms of covered domains, applied vocabularies, and number of triples. A high level of data quality is in the best interest of any data consumer. Linked Data publishers can use various data quality evaluation tools prior to publication of their datasets. But nevertheless, most inconsistencies only become obvious when the data is processed in applications and presented to the end users. Therefore, it is not only the responsibility of the original data publishers to keep their data tidy, but progresses to become a mission for all distributors and consumers of Linked Data, too. My main research topic is the inspection of feedback mechanisms for Linked Data cleansing in open knowledge bases. This work includes a change request vocabulary, the aggregation of change requests produced by various agents, versioning data resources, and consumer notification about changes. The individual components form the basis of a Linked Data Change Management framework.