Six Core Aspects of Semantic AI

Hybrid approach: Semantic AI is the combination of methods derived from symbolic AI and statistical AI. Virtuously playing the AI piano means that for a given use case various stakeholders, not only data scientists, but also process owners or subject matter experts, choose from available methods and tools, and collaboratively develop workflows that are most likely a good fit to tackle the underlying problem. For example, one can combine entity extraction based on machine learning with text mining methods based on semantic knowledge graphs and related reasoning capabilities to achieve the optimal results. Data Quality: Semantically enriched data serves as a basis for better data quality and provides more options for feature extraction. This results in higher precision of prediction & classification calculated by machine learning algorithms.