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Semantic nets in artificial intelligence
- 1. SEMANTIC NETS Submitted to : Ma’am Amita Sharma Efforts by: Harshita Virwani (BCA Sem - 6)
- 2. Meaning of semantic nets • Semantic nets were originally proposed in the early 1960s by M. Ross Quillian to represent the meaning of English words • The basic idea behind semantic nets is that how it carries meaning of the concept and how is related with other concepts • Semantic nets consist of nodes, links (edges) and link labels.
- 3. ELEMENTS USEDIN SEMANTIC NETS • In the semantic network diagram, nodes appear as circles or ellipses or rectangles to represent objects such as physical objects, concepts or situations. • Links or Arcs appear as arrows to express the relationships between objects • Link Labels specify particular relations. • Relationships provide the basic structure for organizing knowledge. • As nodes are associated with other nodes semantic nets are also referred to as associative nets.
- 4. Example of semantic network IS A SUBSET OF SUBSET OF MEMBER OF MEMBER OFSISTER OF
- 5. Example of semantic network
- 6. Example of semantic network Is intended to represent the data: • Tom is a cat. • Tom caught a bird. • Tom is owned by John. • Tom is ginger in color. • Cats like cream. • The cat sat on the mat. • A cat is a mammal. • A bird is an animal. • All mammals are animals. • Mammals have fur.
- 7. INTERSECTION SEARCH In semantic nets, to find relationships among objects are determined by spreading activation out from each of 2 nodes and identify where the activation meets. This process is called intersection search.
- 8. Example of INTERSECTION SEARCH
- 9. Representingnon-binary predicates • Semantic nets are a natural way to represent relationships that would appear as ground instances of binary predicates in logic • For example : • Is a(baseball player, pitcher) • Is a(baseball player, fielder) • Instance(three-finger brown ,pitcher) • Instance(pee-wee Reese ,fielder) • Team(three-finger brown , Chicago cubs) • Team(pee-wee Reese ,Brooklyn dodgers)
- 11. Making some important distinctions • By defining the relationship the complexity of the relation can also be easily represented in semantic nets . • For example : tom weight is 60 kg.
- 12. GREATER THAN
- 13. PARTITIONEDSEMANTICNETS Hendrix developed the partitioned semantic network to represent the difference between the description of an individual object or process and the description of a set of objects. The set description involves quantification. Hendrix partitioned a semantic network whereby a semantic network, loosely speaking, can be divided into one or more networks for the description of an individual. The central idea of partitioning is to allow groups, nodes and arcs to be bundled together into units called spaces – fundamental entities in partitioned networks, on the same level as nodes and arcs
- 14. PARTITIONEDSEMANTICNETS • Suppose that we wish to make a specific statement about a dog, Danny, who has bitten a postman, Peter: – " Danny the dog bit Peter the postman" • Hendrix’s Partitioned network would express this statement as an ordinary semantic network:
- 15. Danny bite B postman Peter Is a Is a Is a assailant victim S1 dog PARTITIONEDSEMANTICNETS
- 16. PARTITIONEDSEMANTICNETS "Every dog has bitten a postman" General Statement dog D bite B postman P is_a is_a is_a assailant victim S1 G form SA is_a
- 17. PARTITIONEDSEMANTICNETS • "Every dog in town has bitten the postman“ General Statement town dog D bite B postman P is_a is_a is_a assailant victim S1 G form SA is_a dog
- 18. "John believes that pizza is tasty" John believes event pizza tasty object property agent is_a object has is_a is_a space
- 19. "Every student loves to party" GS1 General Statement student party love p1 l1 agent is_a is_a receiver is_a is_aS2 GS2 s1 S1 is_a form exists form
- 20. advantages • Easy to visualise and understand. • The knowledge engineer can arbitrarily defined the relationships. • Related knowledge is easily categorised. • Efficient in space requirements. • Related knowledge is easily clustered.
- 21. disadvantages • Inheritance (particularly from multiple sources and when exceptions in inheritance are wanted) can cause problems. • Facts placed inappropriately cause problems. • No standards about node and arc values • This not describes the attributes.