“Alexa, find me a scholarly journal article on nonlinear optics.”
Artificial intelligence (AI) and machine learning (ML) is on a trajectory to quickly replace more traditional means of search. Today’s users ask questions, make statements, and expect search engines to understand and return relevant results in a way that provides greater precision with contextual information and answers.
Researchers and other consumers of scholarly content are among the most advanced and demanding users of information systems and specialized repositories. However, as publishers and content aggregators, we have not yet advanced search to the point at which we can posit to Google or Alexa, “find me a scholarly journal article on nonlinear optics.” But we are advancing toward that goal.
As we prepare our content for semantic search and AI, the adoption of accurate, structured text for the storage and display of scholarly content will make it easier to extract, clean, and structure the data in order to maximize AI, ML and ontological structures.
NFAIS will explore how the scholarly communication community can harness the technologies and capabilities of AI and ML to reshape the scientific research community in its August 29th workshop, Getting Your Content Ready for Semantic Search and AI.
Check out this behind the scenes interview of Bob Kasenchak of Access Innovations:
https://www.youtube.com/watch?
In this interview, Bob will provide a look at where we're headed in regards to artificial intelligence and machine learning, and how we can prepare for the next generation of search.
For more information visit about the presenters and the semantic web, visit:
https://www.nfais.org/index.
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