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 wellaccepted notion of linkset quality has not been yet dened. This paper contributes to overcome this lack by proposing a linkset quality measure. Among the dierent quality dimensions that can be addressed, the proposed measure focuses on completeness. The paper formally denes novel scoring functions and proposes an interpretation of these functions when maintaining and complementing third party datasets
Assessing Linkset Quality for Complementing Third-Party Datasets
R Albertoni;
2013
Abstract
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 wellaccepted notion of linkset quality has not been yet dened. This paper contributes to overcome this lack by proposing a linkset quality measure. Among the dierent quality dimensions that can be addressed, the proposed measure focuses on completeness. The paper formally denes novel scoring functions and proposes an interpretation of these functions when maintaining and complementing third party datasets| File | Dimensione | Formato | |
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