Metadata Crosswalking/ Transforming and Federated Searching in Ex Libris Products Anthony Dellureficio Library Systems Manager The New School University

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

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

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

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

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

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

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

Options  Alter different aspects of the structure:  Data structure  Search structure  Metadata structure  Interpretation of data

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

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

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

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

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

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