Linked SDMX Data

This paper presents transformation from the ISO standard statistical data format SDMX-ML to RDF. URI patterns, interlinking, and publication is proposed for several statistical datasets. The paper is clearly written and the tools can be used for publishing other datasets, although some extensions or specific configuration might be necessary.
The only comments are that the paper is 2 pages too long according to the call. Also it would be better if datasets were published by the providers.

This paper describes a practical workflow for transforming SDMX collections into Linked Data. The authors focus on four relevant statistical datasets:

- OECD: whose mission is to promote policies that will improve the economic and social well-being of people around the world.
- BFS Swiss Statistics, due to the Federal Statistical Office's web portal offering a wide range of statistical information including population, health, economy, employment and education.
- FAO, which works on achieving food security for all to make sure people have regular access to
enough high-quality food.
- ECB, whose main task is to maintain the euro's purchasing power and thus price stability in the
euro area.

Nevertheless, the tool proposed in the paper can be easily used for transforming any other SDMX dataset into Linked Data.

On the one hand, statistical data are rich sources of knowledge currently underexploited. Any new approach is welcome and it describes an effective workflow for transforming SDMX collections into Linked Data. On the other hand, the approach technically sounds. It describes a simple but effective solution based on well-known toolss, guaranteing robustnees and making easy its final integration into existing environments. Thus, the workflow is a contribution by itself and each stage describes how it impacts in the final dataset configuration.

With respect to the obtained datasets, these are clearly described in Sections 7 and 8. These reuse well-known vocabularies and provide interesting interlinkage between them but also with DBpedia, World-Bank, Transparency International and EUNIS. Apache Jena TDB is used to load RDF and Apache Jena Fuseki is used to run the SPARQL endpoint. Datasets are also released as RDF dumps (referenced from Data Hub).

Finally, its relevant for me how scalability problems are addressed, because I think that 12 GB is an excessive amount of memory for processing these datasets (the largest one outputs less 250 million triples). Do you have an alternative for processing larger datasets? Maybe you could partition the original dataset into some fragments: is the tool enough flexible to support it? Please, explain how scalability issues will be addressed to guarantee big datasets to be effectively transformed.

This paper describes the Linked Data datasets, and the process used to generate them, for a set of SDMX-enabled datasets coming from relevant international and country-focused statistical organisations (even some work has been done during the review process on another IMF dataset).

The paper is clearly fitting the special issue call, and the datasets are of good quality, and are made available under open licenses in such linked data format. I particularly like the extensive use of DataCube and Prov-o, and the approach taken for versioning of codelists and concept schemes.

The comments provided in this review are mostly curiosities about some design decisions or requests to make things more clear:
- First of all, a large part of the paper is about the process used to generate the Linked data datasets, but not so much about the datasets themselves or the problems associated to problems in the original SDMX data, which certainly exist still in current sdmx implementations.
- One example is related to the management of some NL descriptions of codes in codelists. These are simply ignored. Could they be handled differently? How manty of the datasets present this situation?
- URI patterns. I find them nicely created and sensible. However, I would have some questions: how do you order dimensions in observations? are you using order in the dimensions in the data cube structures? Why in the owl:Class part do you only use codelistID instead of considering also conceptIDs?
- The interlinkage of sdmx annotations part should be better explained, probably with an example that illustrates how it works.
- The interlinking of datasets should be better explained as well. are all found links correct? I have found many cases of compatible codelists that are produced as separate codelists by stat offices and hence in principle are not linked, but could be. Have you found those cases?