Taking Graphs to the Next Level with Artificial Intelligence and Machine Learning
  Jans Aasman   Jans Aasman
CEO
Franz Inc.
www.franz.com
 


 

Tuesday, April 24, 2018
03:30 PM - 04:30 PM

Level:  Intermediate


Graphs and knowledge management have gained significant visibility with the rebirth of artificial intelligence and emergence of cognitive computing. By combining artificial intelligence, big data, graph databases, and dynamic visualizations, we will discuss deploying graph based AI applications as a means to help predict future events across numerous types of industries.

Knowledge creation via AI and graphs stems from the capability to combine the probability space (i.e. statistical inference on a user’s data) with a knowledge base of comprehensive industry terminology systems. AI using graphs are remarkable not just because of the possibilities they engender, but also because of their practicality. The confluence of knowledge via machine learning, visual querying, graph databases, and big data not only displays links between objects but also quantifies the probability of their occurrence. We believe this approach will be transformative across numerous business verticals.

During the presentation, we will describe the graph-based AI concepts that also incorporate Hadoop, along with analytics via R, SPARK ML, and other AI techniques for practical Enterprise predictive analytics use cases.


Jans Aasman started his career as an experimental and cognitive psychologist, earning his Ph.D. in cognitive science with a detailed model of car driver behavior using Common Lisp and Soar. He has spent most of his professional life in telecommunications research, specializing in intelligent user interfaces and applied artificial intelligence projects. From 1995 to 2004, he was also a part-time professor in the Industrial Design department of the Technical University of Delft. Jans is currently the CEO of Franz Inc., the leading supplier of commercial, persistent, and scalable RDF database products that provide the storage layer for powerful reasoning and ontology modeling capabilities for Semantic Web applications.


 
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