"Many information professionals are concerned about the loss of serendipitous discovery in research pursuits (see this 2015 Kitchen post by Roger Schonfeld.) Depending upon what an individual user knows about a topic when framing their search, our sophisticated systems may either direct the person’s thinking into too narrow a groove — precluding discovery of more loosely relevant items — or inundate the user with too many content possibilities. Online information resources are tightly engineered and so dependent upon well-structured metadata. What’s needed may be a different approach — one that allows the user more latitude in thinking out the scope of the question without becoming too precise.
"Yewno is a semantic-analysis engine that was formally launched at ALA this year, although its creators offered some low-key presentations earlier in 2016 at meetings held by SSP and AAUP. The Yewno technology is run across full-text content, with the system creating a matrix of semantic entities found in each document. Yewno uses a mix of computational semantics, graph theory, and machine learning to retrieve relevant documents without reliance on restrictive conventions imposed by external technology or data format requirements. According to Michael Keller of Stanford, this means that Yewno enables searching of ideas rather than specific expressions, such as keywords. The technology is currently in beta-testing and/or trials at eight institutions: Harvard, Stanford, MIT, the University of Michigan, University of California–Berkeley, Stonehill College, Oxford University, and the Bavarian State Library" (https://scholarlykitchen.sspnet.org/2016/07/13/have-you-looked-at-this-yewno/0