Automating the Categorization of Existing Open Data Impacts Based on Use Case Descriptions

Abstract

Open Government Data (OGD) represents an important source of publicly accessible data originating from the public sector. The primary goal of OGD is to enable transparency, accountability, and the creation of added value. With the increasing volume of data generated by the public sector, there is a strong effort to ensure its accessibility to the public. Research shows that OGD is accessible to the public and also used in the field of economics, where companies utilize business intelligence in a complex global economy. However, economic benefits represent only one of the aspects of the impact of OGD. Recognizing and quantifying the impact of OGD is challenging due to its indirect nature. Studies assessing the impact of OGD include preliminary estimates from surveys, which are limited by staff and funding for OGD-related activities. The challenge lies in recognizing the impact of OGD, for which the literature suggests using data mining and artificial intelligence techniques. The purpose of this research is to confirm the already recognized areas of OGD impact by the European Commission and to guide further research with the proposal of new impact areas. The research followed the CRISP-DM method and utilized various machine learning models to classify OGD use cases. The results indicate the potential of artificial intelligence in recognizing the impacts of OGD, however, there is a need to develop a final and more detailed taxonomy of identified impact areas. The research identified new categories of OGD use that could contribute to a more precise and useful classification of OGD impacts.