While organizing data has always been important, a noticeably profound interest in optimizing information models with Semantic Knowledge graphs has arisen. LinkedIn, AirBnB, in addition to giants Google and Amazon use graphs, but without a model for connecting concepts with rules for membership buyer recommendations and enhanced searchability (follow your nose) capabilities would lack accuracy.
It is a model that supports semantic knowledge graph reasoning, inference, and provenance enablement. Think of an ontology as the brain giving messages to the nervous systems (the knowledge graph). An ontology organizes data into well-defined categories with clearly defined relationships. This model represents a foundational starting point that allows humans and machines to read, understand, and infer knowledge based on its classification. In short, this automatically figures out what is similar and what is different.
We’re asked often, where do I start?
Enter ‘gist’ a minimalist business ontology (model) to springboard transitioning information into knowledge. With more than a decade of refinement grounded in simplicity, ‘gist’ is designed to have the maximum coverage of typical business ontology concepts with the fewest number of primitives and least amount of ambiguity. ‘gist’ is available for free under a Creative Commons license and is being applied and extended within a number of business use cases and utilized by countless industries.
Recently, senior Ontologist Michael Uschold has been sharing an introductory overview of ‘gist’, maintained by Semantic Arts.
One compelling difference from most publicly available ontologies, ‘gist’ has an active governance and best practices community, called the gist Council. The council meets virtually on the first Thursday of every month to discuss how to use ‘gist’ and make suggestions on its evolution.