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The advancement of technology, new methods and emerging data sources have presented both opportunities and challenges to the ABS. While Big Data provides new business opportunities for statistical production, there remain some challenges the ABS needs to overcome. The ABS is exploring semantic web techniques as one possible solution to overcome some of these challenges. This paper describes a prototype Graphically Linked Information Discovery Environment (GLIDE) created using semantic web techniques to better manage statistical information. This paper demonstrates one analytical application of the GLIDE by using it to derive network statistics and create models to distinguish true firm deaths from spurious ones. The ABS has an established process for identifying firm exits, but is not able to distinguish the type of exit – whether it is due to restructuring, merger/takeover or a genuine death. This paper uses multilevel and Bayesian Network models to distinguish true and spurious firm deaths by incorporating network statistics. It is important to account for spurious deaths for statistical production to ensure data quality. The model results also perform much better after incorporating network statistics. We conclude that semantic web is a useful approach for statistical purposes and that network analysis can be used to effectively distinguish true and spurious firm deaths.
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