Short Paper

Metadata Enrichment with Named Entity Recognition using GPT-4

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Abstract

To enhance the user experience and resource discoverability of Infopedia, the Singapore encyclopedia, the National Library Board of Singapore (NLB) uses Generative Pre-trained Transformer 4 (GPT-4) for Named Entity Recognition (NER), aiming to automate metadata enrichment of its digital encyclopedia articles. This initiative leverages GPT-4's capabilities in accurately identifying and incorporating relevant Singaporean entities before integrating them into the NLB's Knowledge Graph, improving recommendations of related resources. An evaluation on a subset of 100 articles demonstrates a precision score of 0.975, indicating high entity detection with minimal inaccuracies. The team acknowledges challenges related to GPT-4’s black-box nature and the potential for non-reproducibility. This effort illustrates the potential of generative AI to streamline metadata enrichment processes, offering a promising avenue for enhancing metadata of digital libraries.

Author information

Ashwin Nair
National Library Board, Singapore
Ee Min Hoon
National Library Board, Singapore
Robin Dresel
National Library Board, Singapore

Cite this article

Nair, A., Min Hoon, E., & Dresel, R. (2024). Metadata Enrichment with Named Entity Recognition using GPT-4. International Conference on Dublin Core and Metadata Applications, 2024. https://doi.org/10.23106/dcmi.952445840

DOI : 10.23106/dcmi.952445840

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