This research studies the effect of integrating Linked Data technologies and generative artificial intelligence (AI) on improving automated cataloging processes in Arab libraries. It analyzes the current adoption of Linked Data in four major Arab libraries (Qatar, Alexandria, Iraq, and Saudi Arabia), and performs an experimental simulation involving 100 MARC records transformed into BIBFRAME format, enriched via Wikidata and AI models (LSTM, ArabianGPT).
Findings show wide disparities in adoption levels, with an average of 35% and a strong correlation with national digital vision indices (r = 0.97). They showed significant improvements: entity extraction accuracy increased from 70% to 87%, semantic links per record rose from 5 to 25, and processing time decreased by 97%, with statistically significant results (p < 0.001).
The study suggests a five-layered hybrid framework for phased implementation, with a realistic roadmap (2026–2032), and a regional initiative to convert 10 million records. So, transitioning to semantic cataloging is both technically feasible and strategically essential for enhancing discoverability and open knowledge integration in Arab libraries.
It recommends Arab library authorities, ministries, and regional bodies initiate pilot Linked Data projects by open-source tools and invest in training specialized semantic cataloging professionals by a coordinated roadmap by 2030.