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.