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Open Data Categorization Based on Formal Concept Analysis
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DOI：10.1109/TETC.2019.2919330.png)
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Abstract
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Government institutions have released a large number of datasets on their open data portals, which are in line with the data transparency and open government initiatives. With the purpose of making it more accessible and visible, these portals categorize datasets based on different criteria like publishers, categories, formats, and descriptions. However, some of this information is often missing, making it impossible to find datasets in all of these ways. As a result, with the number of datasets growing further on the portals, it is getting harder to obtain the desired information. This paper addresses this issue by introducing EODClassifier framework that suggests the best match for the category where a dataset should belong to. It relies on formal concept analysis as a means to generate a data structure that will reveal shared conceptualization originating from tags' usage and utilize it as a knowledge base to categorize uncategorized open datasets.
Keywords:
Portals
Metadata
Government
Text categorization
Software
Formal concept analysis
Machine learning
e-government
open data
data categorization
formal concept analysis
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