Artificial Intelligen ce (AI) and Machine Learning (ML) are umbrella terms for a w ide set of algorithms, technologies, and approaches that make software seem “smart.” Such algorithms can discern patterns in data so that when new data comes in, they can apply patterns to make conclusions ab out new data.
\nKnowledge representation and reasoning (KR) is t he field of AI dedicated to representing information about the world in a f orm that a computer system can utilize. Knowledge Graphs are part of the KR branch of AI. They capture data as well as semantics or the mean ing of data. They enable computers to reason based on the full availab le contextual and conceptual information.
\nKnowledge graphs can help with key challenges of data governance. It is becoming widely accepted tha t Knowledge Graphs are also excellent at guiding and focusing ML and at ser ving as a unifying fabric for the different AI algorithms. Leading com panies who are investing in knowledge graphs include Google, Apple, Amazon, Airbnb, Bloomberg, Facebook, LinkedIn, and Thomson Reuters – and the se are just a few.
\nIn this presentation we will:
\n\n Provide a brief history of Knowledge Graphs\n Demonstrate how they address key challenges of data governance\n Give a concise overview of AI and ML technologies\n Discuss how knowledge graphs provide a powerful platform for both integrate d data governance and strategic enterprise AI/Machine Learning\n Showcase specific real-world examples of how knowledge graphs support rules and learning that add new knowledge that can support further learning in a virtuous cycle\n\n DTSTART:20200324T134500 SUMMARY:How Implementing Data Governance with Knowledge Graphs Enables Ente rprise AI DTEND:20200324T144459 LOCATION: See Description END:VEVENT END:VCALENDAR