| Title: | Open government data topic modeling and taxonomy development |
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| Authors: | ID Ferencek, Aljaž (Author)
ID Kljajić Borštnar, Mirjana (Author) |
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| Files: |  https://www.mdpi.com/2079-8954/13/4/242
 systems-13-00242_(2).pdf (600,19 KB)
MD5: 683D97B08DE1E69730D39A650EB87F95
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| Language: | English |
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| Work type: | Scientific work |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FOV - Faculty of Organizational Sciences in Kranj
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| Abstract: | : The expectations for the (re)use of open government data (OGD) are high. However, measuring their impact remains challenging, as their effects are not solely economic
but also long-term and spread across multiple domains. To accurately assess these impacts,
we must first understand where they occur. This research presents a structured approach
to developing a taxonomy for open government data (OGD) impact areas using machine
learning-driven topic modeling and iterative taxonomy refinement. By analyzing a dataset
of 697 OGD use cases, we employed various machine learning techniques—including
Latent Dirichlet Allocation (LDA), Non-Negative Matrix Factorization (NMF), and Hierarchical Dirichlet Process (HDP)—to extract thematic categories and construct a structured
taxonomy. The final taxonomy comprises seven high-level dimensions: Society, Health,
Infrastructure, Education, Innovation, Governance, and Environment, each with specific
subdomains and characteristics. Our findings reveal that OGD’s impact extends beyond
governance and transparency, influencing education, sustainability, and public services.
Our approach provides a scalable and data-driven methodology for categorizing OGD impact areas compared to previous research that relies on predefined classifications or manual
taxonomies. However, the study has limitations, including a relatively small dataset, brief
use cases, and the inherent subjectivity of taxonomic classification, which requires further
validation by domain experts. This research contributes to the systematic assessment of
OGD initiatives and provides a foundational framework for policymakers and researchers
aiming to maximize the benefits of open data.
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| Keywords: | open government data, topic modeling, taxonomy development, machine learning |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 15.03.2025 |
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| Article acceptance date: | 19.03.2025 |
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| Publication date: | 31.03.2025 |
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| Year of publishing: | 2025 |
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| Number of pages: | str. 1-30 |
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| Numbering: | Vol. 13, issue 4, [article no.] 242 |
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| PID: | 20.500.12556/DKUM-94840  |
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| UDC: | 004.6 |
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| ISSN on article: | 2079-8954 |
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| COBISS.SI-ID: | 231063555 |
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| DOI: | 10.3390/systems13040242 |
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| Publication date in DKUM: | 28.08.2025 |
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| Views: | 0 |
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| Downloads: | 3 |
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| Metadata: |    |
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| Categories: | Misc.
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