We are grateful to our colleague and guest author Omar Farhoud, Sales Manager in the EMEA region, for sharing his perspective on how AI might benefit Arabic-language metadata workflows, as well as risks and limitations to be aware of. For more on implementing AI in metadata workflows, please see our blog series on the topic.
Recent discussions on the OCLC Research blog have explored how artificial intelligence (AI) is beginning to reshape cataloging and metadata workflows, particularly in addressing backlogs and improving efficiency. Conversations like these, however, are often grounded in environments where English-language metadata dominates. Bringing in a Middle Eastern perspective, especially from Arabic-speaking libraries, introduces a different set of conditions shaped by multilingual practice, script diversity, and issues of representation.
Arabic metadata workflows are not edge cases. They represent everyday operational realities across academic, national, and public libraries in the region. As such, they provide a valuable lens through which to examine both the opportunities and the limits of AI in library systems, including the interplay between automated workflows and human oversight.
Arabic metadata as a multilingual workflow
Cataloging in many Middle Eastern libraries is inherently multilingual. Records are typically created in Arabic and English, and in some cases French. This creates a dual responsibility: maintaining consistency within each language while ensuring coherence across scripts.
Within OCLC cataloging environments such as Connexion client, this multilingualism is embedded in the bibliographic record structure. Arabic script fields are paired with romanized equivalents, following established transliteration standards such as ALA-LC. A single intellectual work may therefore exist in parallel representations that must remain aligned over time.
There are also technical considerations. Cataloging in Arabic depends on appropriate input methods, keyboard configurations, and support for non-Latin scripts within MARC environments. These infrastructural elements directly affect both efficiency and data quality.
Transliteration sits at the core of this workflow. While systems provide automated support, outputs frequently require manual correction. Arabic is highly context-sensitive, and small variations in spelling can significantly alter meaning.
Transliteration remains a strong candidate for improvement. Current approaches are largely rule-based. AI models, especially those trained on high-quality bilingual corpora, could offer more context-sensitive transliteration suggestions. However, these would still require validation, reinforcing the need for human oversight.
Discovery expectations and normalization practices
On the discovery side, user expectations introduce an additional layer of complexity. Arabic users expect search systems to handle orthographic variation seamlessly, without requiring precise input.
Recent enhancements in WorldCat Discovery illustrate how this is achieved through rule-based normalization. These include treating diacritics and non-diacritics as equivalent, normalizing character variants (such as different forms of alef), handling prefixes like “ال”, and ignoring elongation characters. Sorting rules are also adapted to reflect Arabic linguistic conventions.
What appears as simple search functionality is underpinned by carefully designed and tested normalization rules. These are deterministic and transparent, refined over time based on real usage patterns.
This is an important benchmark for AI. Any AI-driven approach to metadata creation or discovery must match or exceed this level of linguistic precision. Moreover, AI could extend normalization into cataloging workflows. While discovery systems normalize at query time, catalog records themselves often retain inconsistencies, particularly in legacy data. Machine learning models could assist in identifying and aligning variant forms across large datasets.
From local workflows to shared discovery infrastructures
A broader shift is also taking place in how Arabic collections are positioned within global discovery systems. Aggregated discovery initiatives, such as shared Arabic-language catalogs built on WorldCat infrastructure, reflect a move away from isolated local systems toward more integrated and visible ecosystems.
Field discussions with libraries across the Middle East point to a consistent concern: the global visibility of Arabic scholarship. Fragmentation in discovery and inconsistencies in metadata continue to limit access and representation.
From a strategic perspective, this aligns with broader discussions on the “collective collection” and the role of shared infrastructure in improving resource discovery. AI, when combined with such infrastructure, could help improve metadata consistency at scale and support cross-institutional alignment.
Risks and limitations
Despite these opportunities, the use of AI in Arabic metadata workflows raises several important concerns:
- Language bias remains a significant issue. Many AI models are trained predominantly on English-language data, leading to uneven performance in Arabic. This reflects broader critiques of AI systems as reproducing existing linguistic and cultural imbalances.
- Transliteration introduces additional risks. While rule-based systems are predictable, AI-driven approaches may produce variable outputs that are harder to standardize. This variability can undermine authority control and consistency.
- There is also the risk of losing semantic nuance. Arabic names and terms often carry cultural and contextual meanings that may not be captured by automated systems.
- Normalization itself must be approached carefully. Rule-based normalization is controlled and transparent. AI systems, by contrast, may over-normalize, removing distinctions that are meaningful within the data.
Shared implications for AI implementations and professional practice
Arabic metadata workflows reinforce several broader insights that emerged from OCLC Research’s earlier examination of AI and metadata management.
First, hybrid models are likely to be the most effective. AI can improve efficiency and scalability, but it does not replace the need for professional expertise. Human validation remains essential, particularly in linguistically complex environments. The earlier OCLC Research findings corroborate this, noting “the importance of designing AI implementations as enhancements to human expertise rather than replacements, ensuring that professional development pathways remain robust while leveraging AI’s potential to handle volume and routine tasks.”
Second, there is a need to move beyond English-centric assumptions in system design. Supporting multilingual knowledge infrastructures requires deeper engagement with linguistic diversity at the level of data, standards, and workflows. Again, this observation from Arabic metadata workflows finds a parallel with our earlier findings, which emphasize that “AI systems often lack the deep contextual understanding needed for community-specific terminology or cultural nuances that don’t appear in general training databases.”
Third, metadata enrichment is an area of growing interest. Many Arabic collections lack detailed subject metadata. AI could support the generation of subject headings, summaries, and keywords in Arabic. Our earlier findings also noted the opportunity AI affords for metadata enrichment: for example, institutional repository deposit processes “often fail to supply complete and accurate metadata because students and researchers find metadata creation burdensome and time-consuming.” AI-powered support in areas like subject heading suggestion or automated abstract generation can help close that gap.
Fourth, AI could contribute to backlog reduction by generating draft records or recommendations. This use case was also highlighted in OCLC Research’s earlier findings on AI and metadata workflows: “AI-generated brief records for these materials can enable them to appear in discovery systems earlier, accelerating the process of making hidden collections discoverable and supporting local inventory control. This approach addresses the immediate need for discovery while allowing records to be completed, enriched, or refined over time.”
Arabic metadata workflows present unique features that differ from English language-based systems, which in turn impact specific use cases for AI implementations. Yet as the preceding examples illustrate, there is also general perspective regarding AI-powered metadata workflows that applies equally to Arabic and non-Arabic systems alike. Perhaps most important is the observation that in considering AI implementations, the goal is augmentation rather than replacement, supporting catalogers in focusing their expertise where it adds the most value. There is a tendency in current debates to frame AI adoption as a binary choice between automation and professional control. But this framing is limiting. AI is more usefully understood as part of a continuum of human–machine collaboration, where the question is not just whether to use AI, but how, where, and under what constraints.
Looking ahead
A pragmatic approach is emerging across libraries in the Middle East. Institutions are exploring targeted AI applications, particularly in normalization, enrichment, and transliteration, while maintaining strong human oversight.
There is also an opportunity for collective action. Improving Arabic-language training datasets, strengthening authority control frameworks, and promoting collaboration across institutions will be critical for making AI effective in this space.
Developments in systems such as Connexion and WorldCat Discovery show that progress is already underway. AI can accelerate this work, but only if it is grounded in real workflows and informed by linguistic and cultural expertise.
Ultimately, this is not only a question of efficiency. It is a question of representation. Ensuring that Arabic knowledge is accurately described and fully visible within global discovery systems remains a central challenge and a meaningful test of how inclusive our infrastructures truly are.
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