BRYT: Automated keyword extraction for open datasets

Abstract

In today’s information-driven world, open data is crucial in making valuable structured data freely accessible to the public. However, the absence of quality metadata often hinders the findability and representation of this data. In this study we specifically focus on keywords, proposing a strategy for their automatic generation. In particular, we employed five existing keyword extraction methodologies (BERT, RAKE, YAKE, TEXTRANK, and ChatGPT) and proposed a novel hybrid methodology, named BRYT (read as bright). Our evaluation of these algorithms was conducted using Gestalt String Matching and Jaccard Similarity techniques. We validated our study using a selection of datasets from the EU data portal, specifically choosing those that exhibited potentially high-quality metadata. This included datasets that contained a substantial number of keywords and had comprehensive, relevant metadata. The results showed that 69.1% of the dataset keywords majorly matched (more than 50% or 5 keywords), 24.7% minorly matched (up to 50% or 5 keywords), and 6.2% did not match. The proposed hybrid model, BRYT, outperformed other algorithms in the major matches, while ChatGPT was a close second. YAKE outperformed the others in minor matches, and ChatGPT was again a close second. The evaluations concluded that BRYT consistently extracted more representative keywords in major matches, highlighting its effectiveness in improving findability. This study sets up a favorable field for further representative metadata extraction and population, making the data more findable, discoverable, and accessible.