Validating statistical index data represented in RDF using SPARQL queries

The creation and use of quantitative indexes is a widely accepted practice that has been applied to numerous domains like Bibliometrics (Impact factor), research and academic performance (H-Index or Shanghai rankings), cloud computing (Global Cloud Index, by CISCO), etc. We consider that those indexes and rankings could benefit from a Linked Data approach where the rankings could be seen, tracked and verified by their users. We participated in the Web Index project (http://thewebindex.org), which created an index to measure the Web impact in different countries. The 2012 version offered a data portal1 whose data was obtained by transforming the raw observations and precomputed values from Excel sheets to RDF[3]. In the 2013 version of that data portal, we are working on both validating and computing observations to automatically derivate and populate new values to automate the validation and even the generation of the index from raw data. We have defined a generic vocabulary of computational index structures which could be applied to compute and validate any other kind of index and can be seen as an instance of the RDF Data Cube vocabulary [2]. The validation process employs SPARQL [4] queries to model the different integrity constraints and computation steps in a declarative way. At this moment, we have a running example and a validator which reads and executes the SPARQL queries. Source code and some examples are available at https://github.com/weso/computex. Although our prototype validator has been implemented in Scala, our approach is independent of any programming language as far as it can load and execute SPARQL 1.1 queries. Along the paper we will use Turtle and SPARQL notation and assume that the namespaces have been declared using the most common prefixes found in http://prefix.cc.