The heart of the Cognitive Workbench is its innovative Graph Database, a flexible, typed, semi-structured database that supports complex and intelligent interaction while remaining small enough to run on mobile platforms. Graph databases have a number of advantages for applications, and a long history in artificial intelligence and natural language processing. They reflect more naturally the structure of human linguistic information than more traditional relational and record-oriented databases.
- Graph databases support mixtures of different kinds of data and relations between them without imposing a rigid structure on them.
- A Graph database supports object types more naturally than tables can, including mixtures of strongly and weakly typed data, while still easily supporting the kind of richly structured information found in table-oriented databases.
- Relations between data elements exist separately from the elements themselves and can be extended without complex database operations.
- Relations in graph databases can be interpreted as propositions, lending them to the kinds of reasoning processes that make an application intelligent.
- The contents of graph databases have natural relations of proximity and distance, making it much easier and more intuitive to extract their contents on the basis of ambiguous notions of relatedness, similar to the way that people access memories.
A Graph database is very efficient for storing and managing associative data – data with elements that are connected in some way – and the Cognitive Workbench at its core is an engine for storing and retrieving associated pieces of user data.