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Example: IF-NEEDED facts are called when data of any particular slot is needed. Facets are features of frames which enable us to put constraints on the frames. Slots have names and values which are called facets.įacets: The various aspects of a slot is known as Facets. These slots may be of any type and sizes. It consists of a collection of slots and slot values. Frames are the AI data structure which divides knowledge into substructures by representing stereotypes situations.

These networks are simple and easily understandable.Ī frame is a record like structure which consists of a collection of attributes and its values to describe an entity in the world.

Semantic networks convey meaning in a transparent manner.

Semantic networks are a natural representation of knowledge.

These networks are not intelligent and depend on the creator of the system.

Semantic networks do not have any standard definition for the link names.

These types of representations are inadequate as they do not have any equivalent quantifier, e.g., for all, for some, none, etc.

Semantic networks try to model human-like memory (Which has 1015 neurons and links) to store the information, but in practice, it is not possible to build such a vast semantic network.

It might be possible in the worst case scenario that after traversing the entire tree, we find that the solution does not exist in this network.

Semantic networks take more computational time at runtime as we need to traverse the complete network tree to answer some questions.

Each object is connected with another object by some relation. In the above diagram, we have represented the different type of knowledge in the form of nodes and arcs. This representation consist of mainly two types of relations:Įxample: Following are some statements which we need to represent in the form of nodes and arcs. Semantic networks are easy to understand and can be easily extended. Semantic networks can categorize the object in different forms and can also link those objects. This network consists of nodes representing objects and arcs which describe the relationship between those objects. In Semantic networks, we can represent our knowledge in the form of graphical networks. Semantic networks are alternative of predicate logic for knowledge representation.

Note: Do not be confused with logical representation and logical reasoning as logical representation is a representation language and reasoning is a process of thinking logically. Logical representation technique may not be very natural, and inference may not be so efficient.Logical representations have some restrictions and are challenging to work with.Logical representation is the basis for the programming languages.Logical representation enables us to do logical reasoning.Note: We will discuss Prepositional Logics and Predicate logics in later chapters.

Logical representation can be categorised into mainly two logics: Semantic also involves assigning a meaning to each sentence.Semantics are the rules by which we can interpret the sentence in the logic.It determines which symbol we can use in knowledge representation.Syntaxes are the rules which decide how we can construct legal sentences in the logic.Each sentence can be translated into logics using syntax and semantics. It consists of precisely defined syntax and semantics which supports the sound inference. This representation lays down some important communication rules. Logical representation means drawing a conclusion based on various conditions. Logical representation is a language with some concrete rules which deals with propositions and has no ambiguity in representation. There are mainly four ways of knowledge representation which are given as follows: Next → ← prev Techniques of knowledge representation