Reduced Concept Lattice Usability – An Analysis of Metadata Evolvement Impact

Numerous datasets have been made available on open data portals as a result of the open data initiatives. These portals offer a variety of search possibilities based on the metadata of datasets to facilitate data findability and usability. However, insufficient information frequently has a direct effect on search result quality and, in turn, data discoverability. As a result, methods for completing the missing metadata information—such as missing dataset category values—have become necessary. One of these methods focuses on classifying datasets according to the tags that are applied to them. The foundation of this method is a knowledge base made up of concept lattices produced for every category using the Formal Concept Analysis method. We analyze two sets of reduced concept lattices created for Ireland’s open data portal datasets in 2020 and 2021 and their usability for categorizing new datasets that were available on the portal in 2021 and 2023. Among other results, we will show that concept lattices, although reduced, can be used for a long period and still preserve the accuracy of the categorization algorithm above 90%.