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Title: | The automation of subject indexing and the role of metadata in times of Large Language Models | Authors: | Kasprzik, Anna | Keywords: | subject indexing;automation;machine learning;artificial intelligence;IT infrastructure;metadata;large language models;neuro-symbolic integration | Issue Date: | 15-May-2024 | Publisher: | euroCRIS | Series/Report no.: | CRIS2024: 16th International Conference on Current Research Information Systems (Vienna, Austria, May 15-17, 2024) | Conference: | CRIS2024 – Vienna | Abstract: | So far, virtually every system that facilitates access to and exploration of information resources – library catalogues, discovery systems, research information systems – has metadata as a central component. Accordingly, generating and curating high-quality metadata has always been a core activity of information infrastructure institutions, especially libraries. This includes creating or extracting semantic metadata, also called subject indexing, i.e., the enrichment of metadata records for textual resources with descriptors from a standardized, controlled vocabulary. Due to the proliferation of digital documents, it is no longer possible to annotate every single document intellectually, which generates the need to explore the potentials of automation on every level. |
Description: | Extended abstract presented at the CRIS2024 conference in Vienna, Session 3.2 "New developments".-- Event programme available at https://cris2024.eurocris.org/#programme |
URI: | http://hdl.handle.net/11366/2526 |
Appears in Collections: | Conference |
Files in This Item:
File | Description | Size | Format | |
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Kasprzik-CRIS2024-Automation-subject-indexing.pdf | Extended abstract (PDF) | 124.05 kB | Adobe PDF | View/Open |
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