Implicit vs. Explicit Knowledge for Language Understanding

The most accurate language understanding systems rely on enterprise knowledge to solve business problems of any complexity. Applying such knowledge is foundational to the symbolic AI approach that excels at horizontal use cases such as text analytics, cognitive processing automation (CPA) and smart customer interactions. Typically stored in a knowledge graph, this knowledge takes the form of vocabularies, taxonomies and rules. Such elements provide consistent definitions of terms so their meaning is clear, while rules supply a means of reasoning through this knowledge so that systems actually understand the text they encounter. The application of explicit knowledge consistently provides the most accurate results for language understanding systems.