Metadata Enrichment using AI – First Glance at Research and Findings

Summary

This transcript outlines a discussion on leveraging AI to enhance metadata generation for library cataloging, focusing on the iterative development and improvements of AI-based tools. The team highlights the importance of refining AI capabilities over time, applying AI to flexible metadata fields like subjects, and ensuring that AI doesn't overwrite authorized data. They emphasize using GPT-4 for generating MARC records and maintaining a balance in AI-generated summaries. The session also covers challenges with selecting books for metadata enhancement, managing metadata ecosystems, and ensuring high-quality results while safeguarding against low-quality overrides.

Takeaways

• 😀 AI is improving metadata generation for MARC records by enhancing fields like classification, titles, and authors, with ongoing refinements to increase accuracy.
• 😀 AI-generated metadata is currently in a proof-of-concept phase, with ongoing work to add more subdivisions and fine-tune the results.
• 😀 Librarians have a crucial role in guiding AI, ensuring it produces useful, balanced descriptions that avoid being overly promotional or too concise.
• 😀 The focus is currently on non-fiction works, especially scholarly books, as they present fewer challenges in terms of metadata quality compared to fiction.
• 😀 AI is not expected to replace human catalogers but rather to assist by handling repetitive tasks and scaling the metadata process for larger volumes of books.
• 😀 Not all metadata fields are suitable for AI, with certain fields like titles and authors being strictly controlled for accuracy, while others are more flexible and open to AI intervention.
• 😀 OpenAI's GPT-4 is the primary model being used, and the process involves crafting specific prompts to guide the AI to generate accurate and useful metadata.
• 😀 Continuous testing and iteration of prompts is essential to refine AI performance and ensure that the generated metadata aligns with the needs of librarians and catalogers.
• 😀 Metadata improvements are prioritized for books with missing or incomplete data, with a focus on works that lack quality metadata from publishers.
• 😀 AI-generated metadata improvements are managed carefully, ensuring that authorized data from publishers or catalogers takes precedence over AI-generated data in the final records.

Q & A

What is the main focus of using AI in generating MARC records?

The primary focus of using AI in generating MARC records is to create initial records for books, particularly those lacking metadata. While these AI-generated records may not be perfect, they provide a good starting point for further enhancement and refinement by librarians.

How does the process of generating MARC records with AI work?

AI, specifically GPT-4, is prompted with text from books, and the generated metadata is structured into MARC records. Librarians fine-tune these results by providing specific instructions and guidelines to improve the quality and relevance of the generated records.

What types of books are prioritized for AI-generated MARC records?

Non-fiction and scholarly books are prioritized for AI-generated MARC records due to the higher quality results in these categories. Fiction books are considered more challenging because of the varying nature of their content and classification.

What is the role of librarians in the AI-generated MARC record process?

Librarians are responsible for reviewing and fine-tuning AI-generated MARC records. They ensure the quality and accuracy of the metadata, adjust the tone and formatting, and refine records to meet the specific needs of library cataloging.

Why are certain MARC fields more suitable for AI generation than others?

Certain MARC fields, like title and author names, are more straightforward and factual, making them easier for AI to generate accurately. Other fields, such as subject classification, are more subjective and require human judgment to ensure accuracy and relevance.

What challenges are associated with using AI for metadata enhancement?

One of the main challenges is ensuring that the AI-generated metadata is accurate and does not overwrite authorized data. Additionally, the variability of certain metadata fields, such as subject classifications, requires careful oversight from librarians to ensure consistency and correctness.

How does AI handle prompts to generate MARC records?

AI is given detailed prompts that include instructions about the task at hand, such as generating bibliographic information in a scholarly and formal tone. The process involves testing and iterating on these prompts to achieve the best results, often refining them based on feedback from librarians.

How does the AI handle updates to external metadata sources?

AI-generated metadata will not override authorized data unless it's part of an authorized update from an external provider. Librarians ensure that AI improvements are integrated carefully, and they follow rules to prevent unnecessary repetition or conflicts in metadata.

What approach is taken to prevent AI from producing overly promotional metadata?

To avoid overly promotional descriptions, AI is prompted to generate metadata from the perspective of a librarian, aiming for a balanced, factual tone. In earlier tests, prompts like 'think like a bookstore' led to promotional content, but librarians' guidance refines the tone to be more neutral and accurate.

What is the expected future development of AI in metadata generation for libraries?

As AI technology improves, the goal is to refine the process of generating MARC records, making it more automated and efficient. While the AI-generated metadata is not perfect yet, continuous improvement is expected, with more complex fields and classifications being added over time.