Designing AI Programs for Success - Part 3: Why You Need Ontology and Information Architecture for Artificial Intelligence to Succeed

April 15, 2020 12:00 pm

AI has been getting its fair share of inflated and unrealistic expectations due to a lack of broad understanding of this wide-ranging space by software vendors and customers.  Software tools can be extremely powerful, however the services, infrastructure, data quality, architecture, talent and methodologies to fully deploy in the enterprise are frequently lacking.  This four-part series by Earley Information Science and Pandata will explore a number of issues that continue to plague AI projects and reduce the likelihood of success.  The sessions will provide actionable steps using proven processes to improve AI program outcomes.


In part 3, the topic is "ontology". 

Ontology is a new sounding term if you are not in the AI, library science or semantic space. There is a philosophical context and there is our context. In philosophy, ontology is the study of being. In our information context, it describes a domain of knowledge.

  • Ontology: what is it and why should you care?
  • Why lots of data alone won’t be enough for AI success
  • Determining when an ontology is needed
  • Understanding of how ontologies are developed and applied

Be sure to check out all sessions in this series:

Expert Panel