To SCRY Linked Data: Extending SPARQL the Easy Way

Scientific communities are increasingly publishing datasets on the Web following the Linked Data principles, storing RDF graphs in triplestores and making them available for querying through SPARQL. However, solving domain-specific problems often relies on information that cannot be included in such triplestores. For example, it is virtually impossible to foresee, and precompute, all statistical tests users will want to run on these datasets, especially if data from external triplestores is involved. A straightforward solution is to query the triplestore with SPARQL and compute the required information post-hoc. However, post-hoc scripting is laborious and typically not reusable, and the computed information is not accessible within the original query. Other solutions allow this computation to happen at query time, as with SPARQL Extensible Value Testing (EVT) and Linked Data APIs. However, such approaches can be difficult to apply, due to limited interoperability and poor extensibility. In this paper we present SCRY, the SPARQL compatible service layer, which is a lightweight SPARQL endpoint that interprets parts of basic graph patterns as calls to user defined services. SCRY allows users to incorporate algorithms of arbitrary complexity within standards-compliant SPARQL queries, and to use the generated outputs directly within these same queries. Unlike traditional SPARQL endpoints, the RDF graph against which SCRY resolves its queries is generated at query time, by executing services encoded in the basic graph patterns. SCRY's federation-oriented design allows for easy integration with existing SPARQL endpoints, effectively extending their functionality in a decoupled, tool independent way and allowing the power of SemanticWeb technology to be more easily applied to domain-specific problems.