Knowledge Engineering is a subset of engineering methods and questions within artificial intelligence that seeks to create systems that emulate the judgment, behavior, and expertise of human experts. The most noteworthy creations of knowledge engineering include expert systems and knowledge graphs.
The building blocks of applied knowledge engineering often feature a large topically-specific knowledge base or graph as well as an inference (and/or rules) engine. The inference engine functions on top of the knowledge graph to induct or deduct new facts. A rules engine commonly functions with a set of rules regarding how to apply knowledge within the base or graph. Machine learning is often applied so that expert systems continuously learn and evolve to better emulate human experts.
Diffbot’s Knowledge Graph™ is the largest publicly available example of knowledge engineering at work with over 10 billion entities and 2 trillion inference facts.