Search

• An approach for cleaning and reconciliation of tags in open data portal (ODP) categories using natural language processing methods. • A data structure for connecting ODPs – cross-portal metadata alignment structure generated using ODPs' tags. • Formal context reduction based on semantic similarity measure through means of natural language processing. • Cross-portal dataset categorization - determining appropriate category for a dataset using the metadata alignment structure. • Quality assessment using datasets obtained from Canada's and New Zealand's ODP. Due to openness and transparency initiatives, a vast amount of data is being made publicly available. This data has great significance for business and society. However, it also led to challenges that need to be overcome for this data to reach its full potential. In this paper, we are focusing on the problem of connecting open data portals (ODPs) through metadata alignment. We investigate the available metadata accompanying datasets, especially the part related to categories datasets belong to and tags that closely describe datasets. The methodology we propose is relying on Formal Concept Analysis for the creation of the hierarchical structure used for determining the similarity of tags' usage in different ODPs. We propose such a structure to be used for open data portal metadata alignment. Further, we apply semantic similarity measures to reduce the complexity of the cross-portal data structure while preserving all its characteristics. We demonstrate how our approach can be used for determining dataset category across multiple ODPs aligned using the data structure our approach generates. We envision our approach to improve cross-portal search and metadata enrichment through open data categorization. Lastly, the quality of our approach was tested using datasets obtained from Canada's and New Zealand's ODPs. [ABSTRACT FROM AUTHOR]