Assessing linkset quality for complementing third-party datasets

What is it about?

Linked data best practices are getting extremely popular: various companies and public institutions have started taking advantage of linked data principles for exposing their datasets, and for relating their datasets to those served by third parties. Such enthusiasm is due to the linked data promise of evolving into a Global Data Space. Linksets are sets of links relating datasets and they surely play a fundamental role in this promise. However, a stable and well-accepted notion of linkset quality has not been yet defined. This paper contributes to overcome this lack by proposing a linkset quality measure. Among the different quality dimensions that can be addressed, the proposed measure focuses on completeness. The paper formally defines novel scoring functions and proposes an interpretation of these functions when maintaining and complementing third party datasets.

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Why is it important?

It is one of the first paper explicitly addressing the linkset quality, explicitly distinguishing between linkset and interlinking. It does't focus on the single link correctness, but rather it focus on measuring the data missing generated during the data enrichment via linkset.