Metadata Crosswalking/ Transforming and Federated Searching in Ex Libris Products

1. Metadata Crosswalking/ Transforming and Federated Searching in Ex Libris Products Anthony Dellureficio Library Systems Manager The New School University dellurea@newschool.edu

2. Goals of this Presentation • Define the role of crosswalks in federated searching • Identify the problems created by crosswalks • Explore possible solutions to these problems • Draw conclusions about how to structure metadata and improve federated searches

3. What is a “Crosswalk”? • Federated searches can incorporate many different types of metadata • Crosswalks map descriptive metadata into a uniform schema

4. The Issue with Federated Searching • Increased recall by adding collections • Decreased precision due mismatching data fields • Crosswalks offer a good solution but they are not ideal and they need tweaking

5. Related Products • ExLibris products which gather data from multiple sources (Primo, Digitool, SFX, Metalib, URM) • Products which supply data (Aleph) • Integrated products which gather ExLibris data (Xerxes, Umlaut) • Any other products that contribute or gather data

6. What metadata is crosswalked? • Descriptive metadata • Schemata: MARC, DC, EAD, etc. • Sources: vendor data, locally created descriptive data, harvested data (OAI), public databases

7. Crosswalks in Ex Libris • Table which defines all fields • Table of indexed fields • Table of search fields • Table of display fields • Map between field codes and standard fields

8. Why are crosswalks inadequate? • Not all metadata has an equivalent in another schema • Differing levels of specificity • Lumping metadata • Many standards

9. Options • Alter different aspects of the structure: • Data structure • Search structure • Metadata structure • Interpretation of data

10. Data Structure • Ex. Original cataloging • Pros: total institutional control of data • Cons: conform to standards?, time consuming

11. Search Structure • Ex. Adding database discovery pages • Pros: options for more sophisticated researchers, able to search more specific data • Cons: messy website, confusing to have multiple search pages

12. Metadata Structure • Ex. Parse and lump crosswalk fields • Pros: adds more access points, customized to specific collection • Cons: conforms to standards?, hierarchy problems, slow searches

13. Interpretation of Data • Better search paradigm • Pros: more human, addresses actual problem of data interpretation, not a “work-around” • Cons: requires programming and special knowledge

14. Remaining Problems • Metadata MUST be good! • Access to table files (may need dev box) • Staff and time to alter metadata • May be constrained by old system/structure/data

15. Conclusions • Part of an overall metadata strategy • No single solution • Each institution must know its patrons and how they search • Increased transforming results in increased data loss