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Representation of Syntax,
Semantics and Predicate logics
Prof. Neeraj Bhargava
Kapil Chauhan
Department of Computer Science
School of Engineering & Systems Sciences
MDS University, Ajmer
Text Semantics
• In Natural Language Processing (NLP), semantics is
concerned with the meanings of texts.
• There are two main approaches:
• Propositional or formal semantics: A block of text is to
converted into a formula in a logical language, e.g.
predicate calculus.
• Vector representation. Texts are embedded into a high-
dimensional space.
Representation
 AI agents deal with knowledge (data)
 Facts (believe & observe knowledge)
 Procedures (how to knowledge)
 Meaning (relate & define knowledge)
 Right representation is crucial
 Early realisation in AI
 Wrong choice can lead to project failure
 Active research area
Some General Representations
 Logical Representations
 Production Rules
 Semantic Networks
• Conceptual graphs, frames
 Description Logics
What is a Logic?
 A language with concrete rules
 No ambiguity in representation (may be other errors!)
 Allows unambiguous communication and processing
 Many ways to translate between languages
 A statement can be represented in different logics
 And perhaps differently in same logic
 Expressiveness of a logic
 How much can we say in this language?
 Not to be confused with logical reasoning
 Logics are languages, reasoning is a process (may use logic)
Syntax and Semantics
 Syntax
 Rules for constructing legal sentences in the logic
 Which symbols we can use (English: letters, punctuation)
 How we are allowed to combine symbols
 Semantics
 How we interpret (read) sentences in the logic
 Assigns a meaning to each sentence
 Example: “All lecturers are five foot tall”
 A valid sentence (syntax)
 And we can understand the meaning (semantics)
 This sentence happens to be false (there is a counterexample)
Propositional Logic
 Syntax
 Propositions, e.g. “it is wet”
 Connectives: and, or, not, implies, iff (equivalent)
 Brackets, T (true) and F (false)
 Semantics
 Define how connectives affect truth
 “P and Q” is true if and only if P is true and Q is true
 Use truth tables to work out the truth of statements
Predicate Logic
 Propositional logic combines atoms
 An atom contains no propositional connectives
 Have no structure (today_is_wet, john_likes_apples)
 Predicates allow us to talk about objects
 Properties: is_wet(today)
 Relations: likes(john, apples)
 In predicate logic each atom is a predicate
 e.g. first order logic, higher-order logic
Representation & Logic
 AI wanted “non-logical representations”
 Production rules
 Semantic networks
 Conceptual graphs, frames
 But all can be expressed in first order logic!
 Best of both worlds
 Logical reading ensures representation well-defined
 Representations specialised for applications
 Can make reasoning easier, more intuitive
Assignment
 Explain predicate logic with example.

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Representation of syntax, semantics and Predicate logics

  • 1. Representation of Syntax, Semantics and Predicate logics Prof. Neeraj Bhargava Kapil Chauhan Department of Computer Science School of Engineering & Systems Sciences MDS University, Ajmer
  • 2. Text Semantics • In Natural Language Processing (NLP), semantics is concerned with the meanings of texts. • There are two main approaches: • Propositional or formal semantics: A block of text is to converted into a formula in a logical language, e.g. predicate calculus. • Vector representation. Texts are embedded into a high- dimensional space.
  • 3. Representation  AI agents deal with knowledge (data)  Facts (believe & observe knowledge)  Procedures (how to knowledge)  Meaning (relate & define knowledge)  Right representation is crucial  Early realisation in AI  Wrong choice can lead to project failure  Active research area
  • 4. Some General Representations  Logical Representations  Production Rules  Semantic Networks • Conceptual graphs, frames  Description Logics
  • 5. What is a Logic?  A language with concrete rules  No ambiguity in representation (may be other errors!)  Allows unambiguous communication and processing  Many ways to translate between languages  A statement can be represented in different logics  And perhaps differently in same logic  Expressiveness of a logic  How much can we say in this language?  Not to be confused with logical reasoning  Logics are languages, reasoning is a process (may use logic)
  • 6. Syntax and Semantics  Syntax  Rules for constructing legal sentences in the logic  Which symbols we can use (English: letters, punctuation)  How we are allowed to combine symbols  Semantics  How we interpret (read) sentences in the logic  Assigns a meaning to each sentence  Example: “All lecturers are five foot tall”  A valid sentence (syntax)  And we can understand the meaning (semantics)  This sentence happens to be false (there is a counterexample)
  • 7. Propositional Logic  Syntax  Propositions, e.g. “it is wet”  Connectives: and, or, not, implies, iff (equivalent)  Brackets, T (true) and F (false)  Semantics  Define how connectives affect truth  “P and Q” is true if and only if P is true and Q is true  Use truth tables to work out the truth of statements
  • 8. Predicate Logic  Propositional logic combines atoms  An atom contains no propositional connectives  Have no structure (today_is_wet, john_likes_apples)  Predicates allow us to talk about objects  Properties: is_wet(today)  Relations: likes(john, apples)  In predicate logic each atom is a predicate  e.g. first order logic, higher-order logic
  • 9. Representation & Logic  AI wanted “non-logical representations”  Production rules  Semantic networks  Conceptual graphs, frames  But all can be expressed in first order logic!  Best of both worlds  Logical reading ensures representation well-defined  Representations specialised for applications  Can make reasoning easier, more intuitive
  • 10. Assignment  Explain predicate logic with example.