Knowledge Management and Linked Data Generation in the CIS Statistics Committee

Abstract

This article presents the actions implemented by the Interstate Statistical Committee of the CIS (CIS-STAT) in knowledge management information systems, preparation of linked data and «smart» (semantically rich) metadata as part of the CIS data hub that is under construction. Based on the analysis of international experience and after conducting their own long-term research, the authors set out the purpose behind the work – to increase the efficiency and potential of using statistical data by ensuring an unambiguous and meaningful data interpretation, including in consumer information systems. To reach this goal, the authors proposed new approaches and technologies for building a knowledge management system based on the semantic network, which made it possible to link machine-interpretable semantic models with human-readable knowledge representations. Addressing the objective of organizing knowledge about statistical methodology is a key to increasing the potential for using linked data and enabling collaborative processing of statistical data. The proposed methodological and technological approach is aimed at contextualizing a subject area used to develop linked data and generate «smart» metadata. It also provides new opportunities for consumers to work with statistical data and metadata – their interpretation, meaningful analysis, comparison and joint processing. Along with a description of the systems operating cycle, the article provides a meaningful analysis of the issues of harmonizing statistical terminology, identified by practical work with the «Labor Statistics» domain. Special attention is paid to the role of the expert community in developing a knowledge management system