The second in a series of AI Discovery Sessions for Information, Intelligent Machines, and New Knowledge with Ruggero Gramatica.
How can we transition from the Information Economy to the Knowledge Economy? Traditional approaches based on IT smart technology that leverage indexing, RDF, open link can only marginally tackle the issue. Leveraging the rising of AI and ML together with a new cognitive linguistic approach, I will present the results of applied research which has led to the launch of the first interdisciplinary knowledge graph. Based on the paradigm of conceptual spaces, it builds inferences across concepts and their relationships, always providing precise references to the original literature source and an explanation of the connection. Most importantly, not providing a direct solution to queries but rather leads to infer the reasoning around the answers.
First AI Discovery Session (Posted Earlier This Month)
Please note there is no sound for the first 60 seconds — The digitization of everything in the library initially meant that we could deliver content over the network. But that opportunity introduced a new challenge for discovery: incomplete metadata and lack of context. The explosion of data science practices in recent years has made discovery exponentially more challenging while, at the same time, providing the tools to improve discovery. Investigating the changing research practices across the university, Nicole Coleman, Digital Research Architect for Stanford Libraries, learned that curation of information is more important than it has ever been. To support research, we need to provide librarians with the power tools of artificial intelligence.
See Also: Info About AI Discovery Sessions