Automated Cataloging

1
Automated Cataloging
2
The Problem

• Present cataloging practices are resource
  intensive and frequently redundant
• The volume of new information is expanding
• Resources are scarce
• Many rare and unique materials remain
  uncataloged, and therefore undiscoverable

3
One Step towards a Solution

• Automatic generation of bibliographic metadata,
  e.g. MARC records, using descriptive metadata
  from publishers
• Taking greater advantage of machine processing
  should enable reallocation of resources

4
ONIX

• ONline Information eXchange
• An XML DTD
• An international standard supports Unicode
• Used by many publishers, distributors, and online
  booksellers

5
ltproductidentifiergt ltb221gt02lt/b221gt ltb244gt08160163
56lt/b244gt lt/productidentifiergt ltb012gtBBlt/b012gt ltti
tlegt ltb202gt01lt/b202gt ltb203 textcase
02gtBritish English, A to Zedlt/b203gt lt/titlegt ltco
ntributorgt ltb035gtA01lt/b035gt ltb037gtSchur, Norman
Wlt/b037gt ltb044gtA Harvard graduate in Latin and
Italian literature, Norman Schur attended the
University of Rome and the Sorbonne before
returning to the United States to study law at
Harvard and Columbia Law Schools. Now retired
from legal practise, Mr. Schur is a fluent
speaker and writer of both British and American
English lt/b044gt lt/contributorgt
6
ONIX data structure

• Contains familiar elements, e.g.
• ISBN-10/ISBN-13/EAN.UCC-13
• Title
• Contributor(s)
• Publisher, Imprint, Brand Name
• Edition
• Publication Date
• Language of Product Content

7
Advantages of ONIX

• ONIX records available at an early point in the
  supply chain
• ONIX-MARC crosswalk has been developed by OCLC
• OCLC has already begun a pilot project called
  Next Generation Cataloging

8
Benefits

• Bib records for new publications are generated
  upstream via machine processing and added to
  WorldCat
• Techniques such as data mining can be used to
  enrich publisher metadata