Artificial Intelligence Specialization in the European Union: Underexplored Role of the Periphery at NUTS-3 Level

This study examines the geographical distribution of Artificial Intelligence (AI) research production across European regions at the NUTS-3 level for the period 2015-2024. Using bibliometric data from Clarivate InCites and the Citation Topics classification system, we analyze two hierarchical levels of thematic aggregation: Electrical Engineering, Electronics & Computer Science (Macro Citation Topic 4) and Artificial Intelligence & Machine Learning (Meso Citation Topic 4.61). We calculate the Relative Specialization Index (RSI) and Relative Citation Impact (RCI) for 781 NUTS-3 regions. While major metropolitan hubs such as Paris (IIle-de-France), Warszawa, and Madrid lead in absolute production volume, our findings reveal that peripheral regions, particularly from Eastern Europe and Spain, exhibit the highest levels of relative AI specialization. Notably, we find virtually no correlation between regional specialization and citation impact, identifying four distinct regional profiles: high-impact specialized regions (e.g., Granada, Jaen, Vilniaus), high-volume but low-impact regions (e.g., Bugas, several Polish regions), high-impact non-specialized regions, with Fyn (Denmark) standing out as a remarkable outlier achieving exceptional citation impact (RCI > 4) despite low specialization, and diversified portfolios with selective excellence (e.g., German regions). These results suggest that AI research represents a strategic opportunity for peripheral regions to develop competitive scientific niches, though achieving international visibility requires more than research volume alone.

Improving Tag-Clouds as Visual Information Retrieval Interfaces

Tagging-based systems enable users to categorize web resources by means of tags (freely chosen keywords), in order to refinding these resources later. Tagging is implicitly also a social indexing process, since users share their tags and resources, constructing a social tag index, so-called folksonomy. At the same time of tagging-based system, has been popularised an interface model for visual information retrieval known as Tag-Cloud. In this model, the most frequently used tags are displayed in alphabetical order. This paper presents a novel approach to Tag-Cloud's tags selection, and proposes the use of clustering algorithms for visual layout, with the aim of improve browsing experience. The results suggest that presented approach reduces the semantic density of tag set, and improves the visual consistency of Tag-Cloud layout.