Linked SDMX Data
- Document ID
- CC BY-SA 4.0
- Appeared In
- Semantic Web, Linked Dataset Descriptions, Volume 6, Number 2 / 2015 , Pages 105-112, DOI 10.3233/SW-130123
- Path to high fidelity Statistical Linked Data
As statistical data is inherently highly structured and comes with rich metadata (in form of code lists, data cubes etc.), it would be a missed opportunity to not tap into it from the Linked Data angle. At the time of this writing, there exists no simple way to transform statistical data into Linked Data since the raw data comes in different shapes and forms. Given that SDMX (Statistical Data and Metadata eXchange) is arguably the most widely used standard for statistical data exchange, a great amount of statistical data about our societies is yet to be discoverable and identifiable in a uniform way. In this article, we present the design and implementation of SDMX-ML to RDF/XML XSL transformations, as well as the publication of OECD, BFS, FAO, ECB, IMF, UIS, FRB, BIS, and ABS dataspaces with that tooling.
Categories and Subject Descriptors
While access to statistical data in the public sector has increased in recent years, a range of technical challenges makes it difficult for data consumers to tap into this data at ease. These are particularly related to the following two areas:
- Automation of data transformation of data from high profile statistical organizations.
- Minimization of third-party interpretation of the source data and metadata and lossless transformations.
Development teams often face low-level repetitive data management tasks to deal with someone else's data. Within the context of Linked Data, one aspect is to transform this raw statistical data (e.g., SDMX-ML) into an RDF representation in order to be able to start tapping into what's out there in a uniform way.
The contributions of this article are two-fold. We present an approach for transforming SDMX-ML based on XSLT 2.0 templates and showcase our implementation which transforms SDMX-ML data to RDF/XML. Following this, SDMX-ML data from OECD (Organisation for Economic Co-operation and Development), BFS (Bundesamt für Statistik@de, Swiss Federal Statistical Office@en), FAO (Food and Agriculture Organization of the United Nations), ECB (European Central Bank), and IMF (International Monetary Fund), UIS (UNESCO Institute for Statistics), FRB (Federal Reserve Board), BIS (Bank for International Settlements), ABS (Australian Bureau of Statistics) are retrieved, transformed and published as Linked Data.
As pointed out in Statistical Linked Dataspaces (Capadisli, S., 2012), what linked statistics provide, and in fact enable, are queries across datasets: Given that the dimension concepts are interlinked, one can learn from a certain observation's dimension value, and enable the automation of cross-dataset queries.
Moreover, a number of approaches have been undertaken in the past to go from raw statistical data from the publisher to linked statistical data, as discussed in great detail in Official statistics and the Practice of Data Fidelity (Cyganiak, R., 2011). These approaches go from retrieval of the data by majority; in tabular formats: Microsoft Excel or CSV, tree formats: XML with a custom schema, SDMX-ML, PC-Axis, to transformation into different RDF serialization formats. As far as graph formats go, majority of datasets in those formats not published by the owners. However, there are number of statistical linked dataspaces in the LOD Cloud already.
A number of transformation efforts are performed by the Linked Data community based on various formats. For example, the World Bank Linked Dataspace is based on custom XML that the World Bank provides through their APIs with the application of XSL Templates. The Transparency International Linked Dataspace's data is based on CSV files with the transformation step through Google Refine and the RDF Extension. That is, data sources provide different data formats for the public, with or without accompanying metadata e.g., vocabularies, provenance. Hence, this repetitive work is no exception to Linked Data teams as they have to constantly be involved either by way of hand-held transformation efforts, or in best-case scenarios, it is done semi-automatically. Currently, there is no automation of the transformation step to the best of our knowledge. This is generally due to the difficulty of the task when dealing with the quality and consistency of the statistical data that is published on the Web, as well as the data formats that are typically focused on consumption. Although SDMX-ML is the primary format of the high profile statistical data organizations, it is yet to be taken advantage of.
The publication steps are described in this section.
Dataset Discovery and Statistics
As VoID file is generally intended to give an overview of the dataset metadata i.e., what it contains, ways to access it or query it, each dataspace contains Vocabulary of Interlinked Datasets (VoID) files accessible through their
.well-known/void. Each OECD, BFS, FAO, ECB, IMF, UIS, FRB, BIS, ABS VoID contains locations to RDF datadumps, named graphs that are used in the SPARQL endpoint, used vocabularies, size of the datasets, interlinks to external datasets, as well as the provenance data which was gathered through the retrieval and transformation process. The VoID files were generated automatically by first importing the LODStats information into respective
graph/void into the store, and then a SPARQL
CONSTRUCT query to include all triples as well as additional ones which could be actively created based on the available information in all graphs.
Dataset statistics are generated and are included in the VoID file using LODStats, LODStats – An Extensible Framework for High-performance Dataset Analytics (Demter, J., 2012).
The HTML pages are generated by the Linked Data Pages framework, where Moriarty, Paget, and ARC2 does the heavy lifting for it. Given the lessons learned over the years about Linked Data publishing, there is a consideration to either take Linked Data Pages further (originally written in 2010), or to adapt one of the existing frameworks after careful analysis.
Apache Jena Fuseki is used to run the SPARQL server for the three datasets. SPARQL Endpoints are publicly accessible and read only at their respective
/query locations for OECD, BFS, FAO, ECB, IMF, UIS, FRB, BIS, ABS. Currently, 12000MB of memory is allocated for the single Fuseki server running all datasets.
The code for transformations is at csarven/linked-sdmx, and for retrieval and data loading to RDF store for OECD is at csarven/oecd-linked-data, for BFS is at csarven/bfs-linked-data, for FAO is at csarven/fao-linked-data, for ECB is at csarven/ecb-linked-data, for IMF is at csarven/imf-linked-data, for UIS is at csarven/uis-linked-data, for FRB is at csarven/frb-linked-data, for ABS is at csarven/abs-linked-data. All using the Apache License 2.0.
Announcing the Datasets
For other ways for these datasets to be discovered, they are announced at mailing lists, status update services, and at the Data Hub: OECD is at oecd-linked-data, BFS is at bfs-linked-data, FAO is at fao-linked-data, ECB is at ecb-linked-data, IMF is at imf-linked-data, UIS is at uis-linked-data, FRB is at frb-linked-data, BIS is at bis-linked-data, ABS is at abs-linked-data.
With this work we provided an automated approach for transforming statistical SDMX-ML data to Linked Data in a single step. As a result, this effort helps to publish and consume large amounts of quality statistical Linked Data. Its goal is to shift focus from mundane development efforts to automating the generation of quality statistical data. Moreover, it facilitates to provide RDF serializations alongside the existing formats used by high profile statistical data owners. Our approach to employ XSLT transformations does not require changes to well established workflows at the statistical agencies.
One aspect of future work is to improve the SDMX-ML to RDF transformation quality and quantity. Regarding quality, we aim to test our transformation with further datasets to identify shortcomings and special cases being currently not yet covered by the implementation. Also, we plan the development of a coherent approach for (semi-)automatically interlinking different statistical dataspaces, which establishes links on all possible levels (e.g. classifications, observations). With regard to quantity, we plan to publish statistical dataspaces for Bank for International Settlements (BIS), World Bank and Eurostat based on SDMX-ML data.
The current transformation is mostly based on the generic SDMX format. Since some of the publishers make their data available in compact SDMX format, the transformation toolkit has to be extended. Alternatively, the compact format can be transformed to the generic format first (for which tools exist) and then Linked SDMX transformations can be applied. Ultimately, we hope that Linked Data publishing will become a direct part of the original data owners workflows and data publishing efforts. Therefore, further collaboration on this will expedite the provision of uniform access to statistical Linked Data.
We thank Richard Cyganiak for his ongoing support, as well as graciously offering to host the dataspaces on a server at Digital Enterprise Research Institute. We also acknowledge the support of Bern University of Applied Sciences for partially funding the transformation effort for the pilot Swiss Statistics Linked Data project and thank Swiss Federal Statistical Office for the excellent collaboration from the very beginning.