Friday, 14 September 2018

Semantic Network in Artificial Intelligence

Semantic Network
  • A semantic network or net is a graph structure for representing knowledge in patterns of interconnected nodes and arcs.
  • Computer implementations of semantic networks were first developed for artificial intelligence and machine translation, but earlier versions have long been used in philosophy, psychology, and linguistics.
  • The Giant Global of the Semantic Web is a large semantic network.
  • What is common to all semantic networks is a declarative graphic representation that can be used to represent knowledge and support automated systems for reasoning about the knowledge.
  • Some versions are highly informal, but others are formally defined systems of logic.
  • It is define objects in terms of their association with other objects ex: snow, white, snowman, ice, slippery.
  • Represent knowledge as a graph:
  •  Concepts at lower levels inherit characteristics from their parent concepts.
Six most common kinds of semantic networks:

1: Definitional networks:
  • Emphasize the subtype or is-a relation between a concept type and a newly defined subtype.
  • The resulting network, also called a generalization or subsumption hierarchy, supports the rule of inheritance for copying properties defined for a supertype to all of its subtypes.
  • Since definition are true by definition, the information in these networks is often assumed to be necessarily true.
2 : Assertional networks:
  • Are designed to assert propositions.Unlike definitional network, the information in an assertional network is assumed to be contingently true, unless it is explicitly marked with a modal operator.
  • Some assertional networks have been proposed as models of the conceptual structures underlying natural language semantics.
3 : Implicational networks:
  • Use implication as the primary relation for connecting nodes. They may be used to represent patterns of beliefs, causality, or inferences.
  • Implicational networks emphasize implication, they are capable of expressing all the Boolean connectives by allowing a conjunction of inputs to a propositional node and a disjunction of outputs.
4 : Executable networks:
  • Include some mechanism, such as marker passing or attached procedures, which can perform inferences, pass messages, or search for patterns and associations.
  • Executable semantic networks contain mechanisms that can cause some change to the network itself.
5 : Learning networks:
  • Build or extend their representations by acquiring knowledge from examples.The new knowledge may change the old network by adding and deleting nodes and arcs or by modifying numerical values, called weights, associated with the nodes and arcs.
  • The purpose of learning, both from a natural and AI standpoint, is to create modifications that enable the system to respond more effectively within its environment.
6 : Hybrid networks:
  • Combine two or more of the previous techniques, either in a single network or in separate, but closely interacting networks..
  • System are usually called hybrids if their component languages have different syntax... The most widely used hybrid of multiple network notations is the Unified Modeling Language (UML), which was by designed by three authors....who merged their competing notations.
Inference Mechanism
  • Inheritance
             - e.g. Person by default have 2 legs. How many legs does Mary have ? john?
  • Use of inverse Links (through reification)
              - e.g. hasSister (p,s) and sisterOf (s,p)
Simple semantics nets
  • Nodes are labeled with names (nouns).
  • Arcs labeled with relationships.
  • Special link label "isa" means  "is a".
  • Show membership or subset relationships

  • Simple and transparent inference processes.
  • Ability to assign default values for categories.
  • Ability to include procedural attachment.
  • Simple query language may be too limiting to express complex queries.
  • Does not represent full FOL since it does not provide means to use negation, disjuction, and existential qualification.
  • n-ary functions must be mapped onto binary functions.

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