Long-Term Categorization Capability Assessment of Semantically Reduced Concept Lattices

The findability and usability of open data have never been more important due to the volume of available data. Therefore, methods have been developed to improve the dataset metadata quality, enhancing its findability and usability. One effective approach for filling in missing category information is based on the Formal Concept Analysis method. Since this method relies on a knowledge base consisting of concept lattices produced for each category on an open data portal, the focus of this research is to analyze the usability of the semantically reduced concept lattice over time. In particular, we are focused on (1) the impact of the semantic reduction threshold on the long-term usability and expressiveness of the generated concept lattices and (2) the identification of metadata change indicators that would require recreation of the knowledge bases. We perform this analysis using Ireland’s and Canada’s open data portals—two portals with different approaches to dataset categorization. For both portals, datasets from 2020, 2021, and 2023 are used for knowledge bases creation, while semantically reduced concept lattices are evaluated using six test datasets per portal, containing new datasets from 2020 to 2025. We will show that semantically reduced concept lattices maintain good long-term usability under regular dataset growth. Among other findings, we will demonstrate that dataset categorization based on two-year-old concept lattices achieves over 83% accuracy and that at the category level, even four-year-old reduced concept lattices attain accuracy exceeding 90%.