Linked Open Statistical Data: Relevance and Prospects

After a detailed argumentation of the study’s relevance, this article discusses the prospects for introducing the concept of linked open statistics produced within the framework of a single information environment that ensures efficient production, dissemination, and reuse of statistical and administrative data. The implementation of this qualitatively new concept based on technological innovations and aimed to meet rapidly growing user demands is a key task of digital transformation, defined by the Government of the Russian Federation in the field of official statistics. The major part of open data concerns statistics such as demographic, economic and social indicators. Describing and presenting them in the form of linked open statistics sets an important background for accelerating socio-economic development by introducing new socially significant state, municipal, non-commercial and commercial services/products.

Linked Open Statistical Data (LOSD) allows performing analysis based on a coordinated, integrated information environment as an alternative to using disparate and often controversial data sets. National statistical institutes and government bodies in many countries, together with international organizations, have already chosen the paradigm of linked open statistics. The authors discuss the advantages of this approach, as well as its practical application in international projects.

The article presents the examples and best practices of linked open statistics in a number of publications and strategic documents within the European Statistical System. It also shows the constraints of the linked open statistics development due to the lack of accessible ontologies and standards - the extensions necessary to meet the requirements for classification and management of various concepts in statistics domain. The analysis of projects and initiatives carried out in the article reflects the possibilities and prospects of solving this problem in the field of state statistics. The authors formulate a set of recommendations based both on the analysis of international practice and on the results of their own development experience within the research project «Center of Semantic Integration».

1. Kalampokis E., Tambouris E., Tarabanis K. A Classifi cation Scheme for Open Government Data: Towards Linking Decentralised Data. International Journal of Web Engineering and Technology (IJ WET). 2011;6(3):266-285. Available from: http://www.inderscience.com/offer.php?id=40725.

2. Attard J. et al. A Systematic Review of Open Government Data Initiatives. Government Information Quarterly. 2015;32(4):399-418. Available from: doi: https://doi.org/10.1016/j.giq.2015.07.006.

3. Zuiderwij k A., Janssen M. Open Data Policies, Their Implementation and Impact: A Framework for Comparison. Government Information Quarterly. 2014;31(1):17-19. Available from: doi: https://doi.org/10.1016/j.giq.2013.04.003.

4. Commission Notice - Guidelines on Recommended Standard Licences, Datasets and Charging for the Reuse of Documents. Official Journal of the European Union. C 240/1, 24.7.2014, p. 1-10. Available from: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52014XC0724%2801%29.

5. Capadisli S., Auer S., Ngonga Ngomo A.-C. Linked SDMX Data. Semantic Web. 2015;6(2):105-112. Available from: http://semantic-web-journal.org/system/files/swj454.pdf.

6. Cyganiak R., Hausenblas M., McCuirc E. Official Statistics and the Practice of Data Fidelity. In: Wood D. (ed.) Linking Government Data. New York: SpringerVerlag; 2011. P. 135-151. Available from: doi: https://doi.org/10.1007/978-1-4614-1767-5.

7. Kalampokis E. et al. Open Statistics: The Rise of a New Era for Open Data? In: Scholl H. et al. (eds) Electronic Government.EGOV 2016. Lecture Notes in Computer Science, Vol 9820. Springer, Cham. Available from: doi: https://doi.org/10.1007/978-3-319-44421-5_3.

8. Janssen M., van den Hoven J. Big and Open Linked Data (BOLD) in Government: A Challenge to Transparency and Privacy? Government Information Quarterly. 2015;32(4):363-368. Available from: doi: https://doi.org/10.1016/j.giq.2015.11.007.

9. Akatkin Yu.M., Laikam K.E. Methodological Problems of Unified Description of Statistical Indicators for Inter-Agencies Information Resources. Voprosy Statistiki. 2010;(7):3-9. (In Russ.)

10. Akatkin Yu.M., Laikam K.E. Selected Methodological Questions of Unified Description of Statistical Indicator. Voprosy Statistiki. 2011;(7):11-19. (In Russ.)

11. Janssen M., Charalabidis Y., Zuiderwij k A. Benefits, Adoption Barriers and Myths of Open Data and Open Government. Information System Management. 2012;29(4):258- 268. Available from: doi: https://doi.org/10.1080/1058053 0.2012.716740.

12. Hassani H., Saporta G., Silva E.S. Data Mining and Official Statistics: The Past, the Present, and the Future. Big Data. 2014;2(1):31-43. Available from: doi: https://doi.org/10.1089/big.2013.0038.

13. Bizer C., Heath T., Berners-Lee T. Linked Data: The Story so Far. In: Semantic Services, Interoperability and Web Applications: Emerging Concepts. IGI Global; 2009. P. 205-227. Available from: doi: https://doi.org/10.4018/978-1-60960-593-3.

14. Abellґo A. et al. Fusion Cubes: Towards Self-Service Business Intelligence. International Journal of Data Warehousing and Mining. 2013;9(2):66-88. Available from: doi: https://doi.org/10.4018/jdwm.2013040104.

15. Kalampokis E., Tambouris E., Tarabanis K. Linked open cube analytics systems: Potential and challenges. IEEE Intelligent Systems. 2016;31(5):89-92. Available from: doi: https://doi.org/10.1109/MIS.2016.82.

16. Bischof S. et al. Enriching Integrated Statistical Open City Data by Combining Equational Knowledge and Missing Value Imputation. Journal of Web Semantics. 2018;(48):22-47. Available from: doi: https://doi.org/10.1016/j.websem.2017.09.003.

17. Klímek J. et al. Publication and Usage of Official Czech Pension Statistics Linked Open Data. Journal of Web Semantics. 2018;(48):1-21. Available from: doi: https://doi.org/10.1016/j.websem.2017.09.002.

18. Chaniotaki E. et al. Exploiting Linked Statistical Data in Public Administration: The Case of the Greek Ministry of Administrative Reconstruction. In: 23rd Americas Conference on Information Systems (AMCIS 2017), Boston, MA, USA, August 10-12, 2017. Available from: https://aisel.aisnet.org/amcis2017/eGovernment/Presentations/15/.

19. Kalampokis E., Zeginis D., Tarabanis K. On Modeling Linked Open Statistical Data. Journal of Web Semantics. 2019;(55):56-68. Available from: doi: https://doi.org/10.1016/j.websem.2018.11.002.

20. LOSD Publication and Capacity Building. D1.1 Vision, Stakeholders and Business Case Definition. 15th April 2018. Available from: https://ec.europa.eu/eurostat/cros/system/files/d1.1_vision_stakeholders_and_business_case_v3.pdf.

21. Eurostat. ESS Vision 2020: DIGICOM, Towards an ESS Strategy on (Linked) Open Data. Available from: https://ec.europa.eu/eurostat/cros/content/lod-strategicdocuments_en.

22. Cotton F. et al. XKOS: An SKOS Extension for Statistical Classifications. In: Conference: ISI 2014, 59 World Statistics Congress, Hong Kong, China, August 2014. Available from: https://www.researchgate.net/publication/280740700_XKOS_An_SKOS_Extension_for_Statistical_Classifications/stats.

23. Aracri R.M. et al. Using Ontologies for Official Statistics: The Istat Experience. In: Garrigуs I., Wimmer M. (eds) Current Trends in Web Engineering. ICWE 2017. Lecture Notes in Computer Science, vol 10544. Springer, Cham. Available from: doi: https://doi.org/10.1007/978-3-319-74433-9_15.

24. Generic Statistical Information Model (GSIM). In: High-level Workshop on Modernization of Offi cial Statistics. Nizhni Novgorod, Russian Federation, 10-12 June 2014. Available from: https://www.unece.org/fileadmin/DAM/stats/documents/fund.principles/2014/2-Generic_Statistical_Information_Model_EN.pdf.

25. Cotton F. Core Ontology for Official Statistics. In: Conference of European Statisticians. ModernStats World Workshop 2019. 26-28 June 2019, Geneva, Switzerland. Available from: https://www.unece.org/fileadmin/DAM/stats/documents/ece/ces/ge.58/2019/mtg2/MWW2019_COOS_Cotton_Abstract.pdf.

26. Janev V. et al. Supporting the Linked Data Publication Process with the LOD2 Statistical Workbench. Semantic Web - Interoperability, Usability, Applicability. Under review: submitted on 29.11.2013. Available from: http://www.semantic-web-journal.net/system/files/swj591.pdf.