Keyword searches are the backbone of patron catalog use, and they work pretty well. But librarians know there is a lot of valuable metadata in item records that a keyword search just can’t uncover—and making use of that metadata would lead to more accurate results.
By using BIBFRAME as a basis for organizing information, Vega Discover takes advantage of the intelligence behind library cataloging to expose resources that are relevant to your users without returning unrelated resources that happen to contain the keyword, or missing results that are relevant but may not contain the keyword in the title, author, or subject fields.
For example, the Central Arkansas Library System (CALS) had previously ingested online articles from the Encyclopedia of Arkansas (EOA) into their Sierra ILS, but their external discovery layer didn’t return these articles in results—or at least not in the first few pages of results.
On the very first day CALS tried using Vega Discover, they found relevant EOA articles at the top of their search results! Vega Discover recognized that some EOA articles were about people, and some were about places. Vega automatically generated showcases of Related Resources by grouping articles about places together and articles about people together, exposing one of CALS’ most valuable resources.
How did Vega know to make those intelligent connections? Because linked data creates complex relationships between BIBFRAME entities, exposing relationships between resources, contributors and topics. Vega makes the most of those linked data relationships to show patrons the most relevant resources at the library.
For more details on BIBFRAME, Vega, and CALS’ experience, check out our recent webinar, “Just What I Needed” with Nathan James, the Deputy Executive Director of Technology & Collection Innovative at CALS. https://vimeopro.com/innovativeiii/webinars/video/557728929