Frame-based schemes represent the knowledge in frames that capture descriptive and behavioural information on objects that are represented in the expert system. They share a lot in common with object-oriented programming (frames are derived from semantic networks and later evolved into our modern-day classes and objects.). Frames are organized into hierarchies or networks of frames. Lower level frames can inherit information from upper level frames in the network.
- A frame is a data structure which consists of a collection of slots and slot values. These slots may be of any type and sizes. Slots have names and values which are called facets – features of frames which enable us to put constraints on the frames.
- A frame may consist of any number of slots, and a slot may include any number of facets and facets may have any number of values. A frame is also known as slot-filter knowledge representation in AI.
The frame divides knowledge into substructures by representing stereotypes situations.
An example: A frame-based network and its translation into FOL. Boxed relation names in the network correspond to relations holding for all members of the set of objects [frame]
Advantages and disadvantages of FR are given in the table below:
| Advantages | Disadvantages |
| Makes the programming easier by grouping the related dataComparably flexible and used by many applications in AIVery easy to add slots for new attribute and relations.Easy to include default data and to search for missing valuesEasy to understand and visualize. | Inference engine is not easily processed and cannot be smoothly proceeded A much generalized approach. |