Formal Semantics Purpose: formalize correct reasoning.

Slides:



Advertisements
Similar presentations
Kees van Deemter Matthew Stone Formal Issues in Natural Language Generation Lecture 4 Shieber 1993; van Deemter 2002.
Advertisements

Three Theses of Representation in the Semantic Web
CH-4 Ontologies, Querying and Data Integration. Introduction to RDF(S) RDF stands for Resource Description Framework. RDF is a standard for describing.
The Logic of Quantified Statements
MODEL THEORY FOR DUMMIES Basic idea is to give a mathematical characterization of a ‘world’ in just enough detail to assign a meaning to every expression.
Of 27 lecture 7: owl - introduction. of 27 ece 627, winter ‘132 OWL a glimpse OWL – Web Ontology Language describes classes, properties and relations.
Computability and Complexity 9-1 Computability and Complexity Andrei Bulatov Logic Reminder (Cnt’d)
Artificial Intelligence Modal Logic
1 CA 208 Logic Ex3 Define logical entailment  in terms of material implication  Define logical consequence |= (here the semantic consequence relation.
Presentation on Formalising Speech Acts (Course: Formal Logic)
CS 4700: Foundations of Artificial Intelligence
Sets. Copyright © Peter Cappello Definition Visualize a dictionary as a directed graph. Nodes represent words If word w is defined in terms of word.
Meaning and Language Part 1.
Predicates & Quantifiers Goal: Introduce predicate logic, including existential & universal quantification Introduce translation between English sentences.
RDF Semantics by Patrick Hayes W3C Recommendation Presented by Jie Bao RPI Sept 4, 2008 Part 1 of RDF/OWL Semantics Tutorial.
RDF: Concepts and Abstract Syntax W3C Recommendation 10 February Michael Felderer Digital Enterprise.
 2004 SDU Introduction to the Theory of Computation My name: 冯好娣 My office: 计算中心 430
Relation, function 1 Mathematical logic Lesson 5 Relations, mappings, countable and uncountable sets.
RDF (Resource Description Framework) Why?. XML XML is a metalanguage that allows users to define markup XML separates content and structure from formatting.
LDK R Logics for Data and Knowledge Representation Context Logic Originally by Alessandro Agostini and Fausto Giunchiglia Modified by Fausto Giunchiglia,
An Introduction to Description Logics. What Are Description Logics? A family of logic based Knowledge Representation formalisms –Descendants of semantic.
INF 384 C, Spring 2009 Ontologies Knowledge representation to support computer reasoning.
Math 3121 Abstract Algebra I Section 0: Sets. The axiomatic approach to Mathematics The notion of definition - from the text: "It is impossible to define.
1st-order Predicate Logic (FOL)
Resource Identity and Semantic Extensions: Making Sense of Ambiguity David Booth, Ph.D. Cleveland Clinic (contractor) Semantic Technology Conference 25-June-2010.
Database Management Systems, R. Ramakrishnan1 Relational Calculus Chapter 4.
Pattern-directed inference systems
Chapter 3 RDF and RDFS Semantics. Introduction RDF has a very simple data model But it is quite liberal in what you can say Semantics can be given using.
Sets Define sets in 2 ways  Enumeration  Set comprehension (predicate on membership), e.g., {n | n  N   k  k  N  n = 10  k  0  n  50} the set.
LDK R Logics for Data and Knowledge Representation PL of Classes.
Albert Gatt LIN3021 Formal Semantics Lecture 4. In this lecture Compositionality in Natural Langauge revisited: The role of types The typed lambda calculus.
Formal Methods in Software Engineering 1
ARTIFICIAL INTELLIGENCE [INTELLIGENT AGENTS PARADIGM] Professor Janis Grundspenkis Riga Technical University Faculty of Computer Science and Information.
A Logic of Partially Satisfied Constraints Nic Wilson Cork Constraint Computation Centre Computer Science, UCC.
Logical Agents Chapter 7. Outline Knowledge-based agents Logic in general Propositional (Boolean) logic Equivalence, validity, satisfiability.
1 Introduction to Abstract Mathematics Chapter 2: The Logic of Quantified Statements. Predicate Calculus Instructor: Hayk Melikya 2.3.
1 Introduction to Abstract Mathematics Proofs in Predicate Logic , , ~, ,  Instructor: Hayk Melikya Purpose of Section: The theorems.
1 © The ATHENA Consortium. Resource Description Framework (RDF) A language for making simple statements about things (resources) Statements:
Three words about RDF University of Rome "Tor Vergata" ART: Artificial Intelligence Tor Vergata Author Manuel Fiorelli Date May lastupdate:
Pete Johnston, Eduserv Foundation 16 April 2007 An Introduction to the DCMI Abstract Model JISC.
LDK R Logics for Data and Knowledge Representation Propositional Logic Originally by Alessandro Agostini and Fausto Giunchiglia Modified by Fausto Giunchiglia,
Web Science & Technologies University of Koblenz ▪ Landau, Germany Models of Definite Programs.
ARTIFICIAL INTELLIGENCE [INTELLIGENT AGENTS PARADIGM] Professor Janis Grundspenkis Riga Technical University Faculty of Computer Science and Information.
First-Order Logic Semantics Reading: Chapter 8, , FOL Syntax and Semantics read: FOL Knowledge Engineering read: FOL.
Propositional Logic Rather than jumping right into FOL, we begin with propositional logic A logic involves: §Language (with a syntax) §Semantics §Proof.
Logics for Data and Knowledge Representation ClassL (part 1): syntax and semantics.
Lecture 041 Predicate Calculus Learning outcomes Students are able to: 1. Evaluate predicate 2. Translate predicate into human language and vice versa.
Logics for Data and Knowledge Representation ClassL (part 1): syntax and semantics.
Linked Data & Semantic Web Technology The Semantic Web Part 7. RDF Semantics Dr. Myungjin Lee.
Artificial Intelligence Logical Agents Chapter 7.
Introduction to Set Theory (§1.6) A set is a new type of structure, representing an unordered collection (group, plurality) of zero or more distinct (different)
The Principle of ImageCollection by Albert Ziegler University of Munich (LMU)
Sets.
The Semantic Web By: Maulik Parikh.
Semantics In propositional logic, we associate atoms with propositions about the world. We specify the semantics of our logic, giving it a “meaning”. Such.
Ontology.
CS201: Data Structures and Discrete Mathematics I
Logics for Data and Knowledge Representation
Logics for Data and Knowledge Representation
Lesson 5 Relations, mappings, countable and uncountable sets
1st-order Predicate Logic (FOL)
Lesson 5 Relations, mappings, countable and uncountable sets
Discrete Mathematics Lecture 4 & 5: Predicate and Quantifier
Chapter 3 RDF and RDFS Semantics
Ontology.
Logics for Data and Knowledge Representation
Knowledge Representation I (Propositional Logic)
Discrete Mathematics Lecture 4 & 5: Predicate and Quantifier
CS201: Data Structures and Discrete Mathematics I
1st-order Predicate Logic (FOL)
Presentation transcript:

Formal Semantics Purpose: formalize correct reasoning

Propositions Statements that are the basic element of reasoning – Defined in different kinds of logic (contexts): e.g., RDF triples; first order predicate logic The set of all propositions ( P ) – P has many specific propositions: p  P – Some propositions (e.g., p3 and p4) may be the logical consequences of other propositions (e.g., p1 and p2) {p1, p2} {p3, p4} Where is the entailment relation, relating sets of propositions to each other A logic (L) is composed of a set of propositions ( P ) with the entailment relation ( ), i.e., L = ( P, )

Model Theoretic Semantics Interpretation, I (think of it as potential ‘realities’ or ‘worlds’) In each type of logic, we use certain mathematical structures as interpretation We want to know if a specific interpretation (I) satisfies a specific proposition p  P (i.e., I p) which reads: ‘I model of p’ I is a model of P (i.e., I P) if it is a model for every p  P Let P and P’ be subsets of set P (i.e., P  P and P’  P ) P’ is entailed by P (i.e., P P’) iff every interpretation (I) satisfying all individual sentences (p) of P (i.e., I P) is also a model of (i.e., satisfies) every sentence p’ from P’ (I P’), for example: igneous_rock rocki.e., if proposition igneous rock entails rock, then If interpretation I satisfies igneous rock (I igneous rock), it must also satisfy (be a model of) the proposition rock (i.e., I rock)

Entailment relation via models logical entailment (I p) reads: I is a model of p, i.e., I satisfies p p 1 : volcanic p 2 : clastic p 3 : volcaniclastic {p 1, p 1 } {p 3 } Models of p 1 (I p 1 ) Propositions Interpretations Models of p 2 (I p 2 ) Models of p 3 (I p 3 ) {p 1, p 2 } {p 3 } iff (I p 1 and I p 2 ), I also satisfies p 3, i.e., I p 3 That is, p 1, p 2 entail p3 Iff the interpretation I satisfies (i.e., is a model of) both p 1 and p 2 as well as p 3.

Simple Interpretations Vocabulary (V) is an arbitrary set of URIs and literals RDF triples relate resources (R) via properties (P) An interpretation (  ) of triples has two sets:  R and  P  R: non-empty set of resources (domain or universe of discourse)  P: the set of properties of  (may overlap  R)  EXT a function that assigns a set of pairs from  R to each property I S a function mapping URIs from V into the union of the sets  R and  P.  L a function from the typed literals from V into the set of  R LV a particular subset of  R called the set of literal values

Interpretation function. 