Equity

EBSCO users span the globe, and we understand that not only are languages different, but so are the ways in which folks identify and interact with data. 

Each user has their own mental model comprised of their experiences, their culture, their language, and their needs. This is why supporting equitable search has been a priority for EBSCO for decades, first mapping publisher subject headings across databases to ensure no matter which publisher subject vocabulary a user was familiar with, they could still retrieve content, even if the subject tags weren't a synonymous match. This mapping is called the Unified Subject Index, or USI. 

EBSCO took this a step further and added all National Library Subject Authorities and the most authoritative government and linked data vocabularies to the USI, which created one of the largest multilingual mappings of scholarly vocabularies in the world, covering over 280 languages and dialects. Even better, that same year we gathered billions of natural language terms to map to their controlled term equivalencies within the USI. 

EBSCO also is using AI in search to help “decode” the search experience for those new to academic search. Complex advanced queries are still the standard for advanced research, but many struggle to find a foothold if they are unfamiliar with research and their library resources. 

The new Natural Language Search mode in EBSCO Discovery Service and EBSCOhost helps break down the barriers to entry by helping parse the query into more meaningful noun-phrase chunks that helps the EBSCO proprietary search engine retrieve not only relevant results, but contextually specific results as well, honoring the users intended query. 

This helps users who may not know how to formulate a complex query to still retrieve results that will help them along their research journey. This helps level the field and allows more folks to get going on their research, without specialized knowledge, making for a more equitable search experience.