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Semantic Networks and Frames in ai

Semantic Networks and Frames in AI

Semantic networks are a natural representation of knowledge. Semantic networks convey meaning transparently. These networks are simple and easily understandable. A frame is a record-like structure that consists of a collection of attributes and its values to describe an entity in the world.

Here we are going to discuss Semantic Networks and frames in AI.

Semantic Networks and Frames in AI

Semantic Network

  • Semantic networks are alternatives to predicate logic for knowledge representation.
  • In semantic networks, we can represent our knowledge in the form of graphical networks.
  • This network consists of nodes representing objects and arcs which describe the relationship between those objects.
  • Semantic networks can categorize objects in different forms and can also link those objects.
  • Semantic networks are easy to understand and can be easily extended.
  • A semantic network is a simple representation scheme that uses a graph of labeled nodes and labeled directed arcs to encode knowledge:
  • Node – objects, concepts, events
  • Arcs – relationships between nodes
  • Graphical depiction associated with semantic networks is a big reason for their popularity.
  • This representation consists of mainly two types of relations:
    • IS-A relation (Inheritance)
    • Kind-of-relation

 Example

Statements

Semantic Networks and Frames
  • Jack is a dog.                                  
  • Jack is a mammal.
  • Jack is owned by Joy.
  • Jack is black colored.
  • All mammals are animals.

Nodes and Arcs

Semantic Networks and Frames in Artificial Intelligence
  • Mother (jhon, sue)
  • Agr (jhon, 5)
  • Wife (sue, max)
  • Age (max, 34)
  • Arcs define binary relations which hold between objects denoted by the nodes. 

Advantages of Semantic Networks

  • Semantic networks are a natural representation of knowledge.
  • They convey meaning transparently.
  • These networks are simple and easily understandable. 
  • They can help represent events and natural language sentences. 
  • The semantics, i.e. real-world meanings, are identifiable.

Drawbacks of Semantic Networks

  • Semantic networks take more computational time at runtime as we need to traverse the complete network tree to answer some questions.
  • They try to model human-like memory to store the information, but in practice, it is not possible to build such a vast semantic network. 
  • These types of representations are inadequate as they do not have any equivalent quantifier, e.g., for all, for some, none, etc.
  • Semantic networks do not have any standard definition for the link names.
  • These networks are not intelligent and depend on the creator of the system.  

Frames in AI

  • Frames are derived from semantic networks and later evolved into our modern-day classes and objects.
  • A single frame is not very useful.
  • A frame system consists of a collection of frames that are connected. 
  • In the frame, knowledge about an object or event can be stored together in the knowledge base. 
  • It’s a type of technology that is widely used in various applications including Natural language processing and machine visions.

Frame Representation

  • A frame is a record-like structure that consists of a collection of attributes and its values to describe an entity in the world.
  • Frames are the AI data structure that divides knowledge into substructures by representing stereotypical situations.
  • It consists of a collection of slots and slot values.
  • These slots may be of any type and size.
  • Slots have names and values which are called facets.    

Facets

  • The various aspects of a slot are known as Facets
  • Facets are features of frames that enable us to put constraints on the frame.
  • A frame is also known as slot-filler knowledge representation.
  • A slot that is filled at the class level represents attributes that are common to all members of that class.
  • Similarly, if it is filled at the instance level, it indicates that the value of that attribute varies among members of that class.
  • Slots may be filled with values or with pointers to other objects.

Example 1.

The book is the object, the slot name is the attribute, and the filler is the value.

SlotsFillers
TitleArtificial Intelligence
GenreComputer Science
AuthorPrentice Hall
Year2020 (4th Ed.)
Pages900

Example 2.

Paul is an engineer by profession, and his age is 22, he lives in a city in California, and the country is the US. So, the following is the frame representation for this:

SlotsFillers
NamePaul
Profession Engineer
Age22
CityCalifornia
CountryUS

Advantages of Frame Representation

  • The frame knowledge representation makes the programming easier by grouping the related data.
  • It is comparable, flexible, and used by many applications in AI.
  • It is very easy to add slots for new attributes and relations. 
  • It is easy to include default data and to search for missing values. 
  • Easy to understand and visualize. 

Disadvantages of Frame Representation 

  •  The inference mechanism cannot be smoothly proceeded by frame representation.
  • Frame representation has a much more generalized approach.

Author: The Cloudflare Editorial

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