Linked Data Wrapper Curation: A Platform Perspective

Tracking #: 1659-2871

This paper is currently under review
Authors: 
Iker Azpeitia
Jon Iturrioz
Oscar Díaz

Responsible editor: 
Ruben Verborgh

Submission type: 
Full Paper
Abstract: 
Linked Data Wrappers (LDWs) turn Web APIs into RDF end-points, leveraging the LOD cloud with current data. This potential is frequently undervalued, regarding LDWs as mere by-products of larger endeavors, e.g. developing mashup applications. However, LDWs are mainly data-driven, not contaminated by application semantics, hence with an important potential for reuse. If LDWs could be decoupled from their breakout projects, this would increase the chances of LDWs becoming truly RDF end-points. But this vision is still under threat by LDW fragility upon API upgrades, and the risk of unmaintained LDWs. LDW curation might help. Similar to dataset curation, LDW curation aims to clean up datasets but, in this case, the dataset is implicitly described by the LDW definition, and “stains” are not limited to those related with the dataset quality but also include those related to the underlying API. This requires the existence of LDW platforms that leverage existing code repositories with additional functionalities that cater for LDW definition, deployment and curation. This paper contributes to this vision through: (1) identifying a set of requirements for LDW platforms; (2) instantiating these requirements for Yahoo’s YQL; and (3), validating the extent to which this approach facilitates LDW curation.
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