Guest Post — Scholarly AI Search Shortcomings and the Need for Better Metadata
AI scholarly search tools often miss important literature due to incomplete metadata. Better full-text-derived metadata could significantly improve discovery.
Peter Webster is the Associate University Librarian for Information Technology Services at Saint Mary’s University. He has an MLS degree from Dalhousie University and a BA in History and English from the University of Alberta.
Peter has played a leading role in the development of Library technology, digital library collections, and library services at his university. He has also made contributions to the development of academic library infrastructure at the regional and national levels in Canada. He has served in management and project roles with Nova Scotia and Atlantic Canadian academic library consortia (Novanet and the Council of Atlantic Academic Libraries (CAAL-CPUA), the Canadian Research Knowledge Network (CRKN), and the Digital Research Alliance of Canada.
Peter has written numerous articles, reports, and book contributions on academic library technology-related subjects. He has presented at conferences, including the IASSIST, EDUCAUSE, Computers in Libraries, Library and Information Technology (LITA) Forum, and ACCESS.
AI scholarly search tools often miss important literature due to incomplete metadata. Better full-text-derived metadata could significantly improve discovery.