Many organizations are sitting on a wealth of unstructured text. There are many OpenSource and free tools than can help build large scale associative networks in either unsupervised or semi-supervised ways.
With exponentially increasing volumes of information, much information is being ranked or suggested by popularity. That may effectively ‘censor’ some information through its obscurity. With an increasing need/intent for search engines to ‘show me something I don’t already know’ there appears a need to revisit ‘relevance’ algorithms. What is most popular, is not necessarily what is most interesting.
Search engines may be increasingly the way in which ‘we come to know’. If a corpus is the starting point, allowing the user to explore associative networks in various ways (not just by popularity), as a series of click-able facets, may mitigate the issues presented with a classic search box, where a searcher may be hampered to find out what they don’t know by their own existing knowledge of keywords. In other words, the agency of the searcher using traditional search engines may limit their ability to discover something they have no advance knowledge of. Exploiting associative networks may be a useful way of discovering new knowledge.
More here at LinkedIn: https://www.linkedin.com/pulse/teaching-machines-subject-domain-paul-cleverley?published=u
Slides and references: http://www.slideshare.net/phcleverley/teaching-machines-about-a-subject-domain