LDK R Logics for Data and Knowledge Representation Description Logics: family of languages.

Slides:



Advertisements
Similar presentations
Charting the Potential of Description Logic for the Generation of Referring Expression SELLC, Guangzhou, Dec Yuan Ren, Kees van Deemter and Jeff.
Advertisements

Query Answering based on Standard and Extended Modal Logic Evgeny Zolin The University of Manchester
CS848: Topics in Databases: Foundations of Query Optimization Topics covered  Introduction to description logic: Single column QL  The ALC family of.
OWL - DL. DL System A knowledge base (KB) comprises two components, the TBox and the ABox The TBox introduces the terminology, i.e., the vocabulary of.
An Introduction to Description Logics
1 A Description Logic with Concrete Domains CS848 presentation Presenter: Yongjuan Zou.
INTRODUCTION TO ARTIFICIAL INTELLIGENCE Massimo Poesio LECTURE 4: Semantic Networks and Description Logics.
Description Logics. Outline Knowledge Representation Knowledge Representation Ontology Language Ontology Language Description Logics Description Logics.
Part 6: Description Logics. Languages for Ontologies In early days of Artificial Intelligence, ontologies were represented resorting to non-logic-based.
ANHAI DOAN ALON HALEVY ZACHARY IVES Chapter 12: Ontologies and Knowledge Representation PRINCIPLES OF DATA INTEGRATION.
FiRE Fuzzy Reasoning Engine Nikolaos Simou National Technical University of Athens.
LDK R Logics for Data and Knowledge Representation Description Logics as query language.
An Introduction to Description Logics. What Are Description Logics? A family of logic based Knowledge Representation formalisms –Descendants of semantic.
Tableau Algorithm.
Ming Fang 6/12/2009. Outlines  Classical logics  Introduction to DL  Syntax of DL  Semantics of DL  KR in DL  Reasoning in DL  Applications.
Query Answering Based on the Modal Correspondence Theory Evgeny Zolin University of Manchester Manchester, UK
LDK R Logics for Data and Knowledge Representation ClassL (part 3): Reasoning with an ABox 1.
Presented by:- Somya Gupta( ) Akshat Malu ( ) Swapnil Ghuge ( ) Franz Baader, Ian Horrocks, and Ulrike Sattler.
Topics in artificial intelligence 1/1 Dr hab. inż. Joanna Józefowska, prof. PP Reasoning and search techniques.
An Introduction to Description Logics (chapter 2 of DLHB)
Semantic web course – Computer Engineering Department – Sharif Univ. of Technology – Fall Description Logics: Logic foundation of Semantic Web Semantic.
Logics for Data and Knowledge Representation Exercises: DL.
LDK R Logics for Data and Knowledge Representation PL of Classes.
LDK R Logics for Data and Knowledge Representation Description Logics (ALC)
More on Description Logic(s) Frederick Maier. Note Added 10/27/03 So, there are a few errors that will be obvious to some: So, there are a few errors.
LDK R Logics for Data and Knowledge Representation Description Logics.
Logics for Data and Knowledge Representation ClassL (part 1): syntax and semantics.
Knowledge Representation in Description Logic. 1. Introduction Description logic denotes a family of knowledge representation formalisms that model the.
DL Overview Second Pass Ming Fang 06/19/2009. Outlines  Description Languages  Knowledge Representation in DL  Logical Inference in DL.
4-Nov-05 CS6795 Semantic Web Techniques 1 Description Logic – 2.
LDK R Logics for Data and Knowledge Representation ClassL (Propositional Description Logic with Individuals) 1.
Logics for Data and Knowledge Representation Exercises: ClassL Fausto Giunchiglia, Rui Zhang and Vincenzo Maltese.
LDK R Logics for Data and Knowledge Representation ClassL (part 2): Reasoning with a TBox 1.
Description Logics Dr. Alexandra I. Cristea. Description Logics Description Logics allow formal concept definitions that can be reasoned about to be expressed.
ece 627 intelligent web: ontology and beyond
Complexity of Reasoning
Knowledge Repn. & Reasoning Lec #11+13: Frame Systems and Description Logics UIUC CS 498: Section EA Professor: Eyal Amir Fall Semester 2004.
Charting the Potential of Description Logic for the Generation of Referring Expression SELLC, Guangzhou, Dec Yuan Ren, Kees van Deemter and Jeff.
Logics for Data and Knowledge Representation ClassL (part 1): syntax and semantics.
Knowledge Representation and Reasoning University "Politehnica" of Bucharest Department of Computer Science Fall 2011 Adina Magda Florea
Presented by Kyumars Sheykh Esmaili Description Logics for Data Bases (DLHB,Chapter 16) Semantic Web Seminar.
Of 29 lecture 15: description logic - introduction.
Logics for Data and Knowledge Representation Exercises: DL.
Ontology Technology applied to Catalogues Paul Kopp.
Knowledge Representation and Inference Dr Nicholas Gibbins 32/3019.
Logics for Data and Knowledge Representation ClassL (part 1): syntax and semantics.
LDK R Logics for Data and Knowledge Representation Description Logics.
1 Representing and Reasoning on XML Documents: A Description Logic Approach D. Calvanese, G. D. Giacomo, M. Lenzerini Presented by Daisy Yutao Guo University.
Logics for Data and Knowledge Representation
Logics for Data and Knowledge Representation
Description Logics.
ece 720 intelligent web: ontology and beyond
Logics for Data and Knowledge Representation
Logics for Data and Knowledge Representation
Logics for Data and Knowledge Representation
Logics for Data and Knowledge Representation
Logics for Data and Knowledge Representation
Logics for Data and Knowledge Representation
Logics for Data and Knowledge Representation
Logics for Data and Knowledge Representation
Logics for Data and Knowledge Representation
Logics for Data and Knowledge Representation
Description Logics.
Logics for Data and Knowledge Representation
Description logics (book, page 456)
Logics for Data and Knowledge Representation
A Tutorial Summary of Description Logic and Hybrid Rules
CIS Monthly Seminar – Software Engineering and Knowledge Management IS Enterprise Modeling Ontologies Presenter : Dr. S. Vasanthapriyan Senior Lecturer.
Logics for Data and Knowledge Representation
Logics for Data and Knowledge Representation
Presentation transcript:

LDK R Logics for Data and Knowledge Representation Description Logics: family of languages

Outline  Overview  Syntax: the DL family of languages  Semantics  TBox  ABox 2

Overview  Description Logics (DLs) is a family of KR formalisms 3 TBox ABox RepresentationReasoning  Alphabet of symbols with two new symbols w.r.t. ClassL:  ∀ R (value restriction)  ∃ R (existential quantification) R are atomic role names OVERVIEW :: SYNTAX :: SEMANTICS :: TBOX :: ABOX

AL (Attributive language)  Formation rules: ::= A | B |... | P | Q |... | ⊥ | ⊤ ::= | ¬ | ⊓ | ∀ R.C | ∃ R. ⊤ with no ⊔, ∃ R. ⊤ = limited existential quantifier, ¬ on atomic only  Person ⊓ Female “persons that are female”  Person ⊓ ∃ hasChild. ⊤ “(all those) persons that have a child”  Person ⊓ ∀ hasChild. ⊥ “(all those) persons without a child”  Person ⊓ ∀ hasChild.Female “persons all of whose children are female” 4 OVERVIEW :: SYNTAX :: SEMANTICS :: TBOX :: ABOX

ALU (AL with disjunction)  Formation rules: ::= A | B |... | P | Q |... | ⊥ | ⊤ ::= | ¬ | ⊓ | ∀ R.C | ∃ R. ⊤ | ⊔ with ⊔  Person ⊓ (Mother ⊔ Father) “the people who are parents”  Apple ⊓ (Red ⊔ Yellow) “red and yellow apples” 5 OVERVIEW :: SYNTAX :: SEMANTICS :: TBOX :: ABOX

ALE (AL with extended existential)  Formation rules: ::= A | B |... | P | Q |... | ⊥ | ⊤ ::= | ¬ | ⊓ | ∀ R.C | ∃ R. ⊤ | ∃ R.C with ∃ R.C (full existential quantification)  Parent ⊓ ∃ hasChild.Female “parents having at least a daughter” 6 OVERVIEW :: SYNTAX :: SEMANTICS :: TBOX :: ABOX

ALN (AL with number restriction)  Formation rules: ::= A | B |... | P | Q |... | ⊥ | ⊤ ::= | ¬ | ⊓ | ∀ R.C | ∃ R. ⊤ | ≥nR | ≤nR ≥nR (at-least number restriction) ≤nR (at-most number restriction)  Parent ⊓ ≥2 hasChild “parents having at least two children” 7 OVERVIEW :: SYNTAX :: SEMANTICS :: TBOX :: ABOX

ALC (AL with full concept negation)  Formation rules: ::= A | B |... | P | Q |... | ⊥ | ⊤ ::= | ¬ | ⊓ | ∀ R.C | ∃ R. ⊤ with full concept negation   (Mother ⊓ Father) “it cannot be both a mother and father” 8 OVERVIEW :: SYNTAX :: SEMANTICS :: TBOX :: ABOX

AL’s extensions  By extending AL with any subsets of the above constructors yields a particular DL language.  Each language is denoted by a string of the form: AL[U][E][N][C], where a letter in the name stands for the presence of the corresponding constructor. 9 OVERVIEW :: SYNTAX :: SEMANTICS :: TBOX :: ABOX

AL’s sub-languages  By eliminating some of the syntactical symbols and rules, we get some sub-languages of AL  ClassL: the most important sub-language obtained by elimination in the AL family  FL- and FL0 (where FL = frame language) 10 OVERVIEW :: SYNTAX :: SEMANTICS :: TBOX :: ABOX

From AL to ClassL  ALUC with the elimination of roles ∀ R.C and ∃ R. ⊤  Formation rules: ::= A | B |... | P | Q |... | ⊥ | ⊤ ::= | ¬ | ⊓ | ⊔  The new language is a description language without roles which is ClassL (also called propositional DL) NOTE: So far, we are considering DL without TBOX and ABox. 11 OVERVIEW :: SYNTAX :: SEMANTICS :: TBOX :: ABOX

AL’s Contractions: FL- and FL0  FL- is AL with the elimination of ⊤, ⊥ and   Formation rules: ::= A | B |... | P | Q |... ::= | ⊓ | ∀ R.C | ∃ R. ⊤  FL0 is FL- with the elimination of ∃ R. ⊤  Formation rules: ::= A | B |... | P | Q |... ::= | ⊓ | ∀ R.C 12 OVERVIEW :: SYNTAX :: SEMANTICS :: TBOX :: ABOX

AL* Interpretation (∆,I)  I( ⊥ ) = ∅ and I( ⊤ ) = ∆ (full domain, “Universe”)  For every concept name A of L, I(A) ⊆ ∆  I(¬C) = ∆ \ I(C)  I(C ⊓ D) = I(C) ∩ I(D)  I(C ⊔ D) = I(C) ∪ I(D)  For every role name R of L, I(R) ⊆ ∆ × ∆  I( ∀ R.C) = {a ∈ ∆ | for all b, if (a,b) ∈ I(R) then b ∈ I(C)}  I( ∃ R. ⊤ ) = {a ∈ ∆ | exists b s.t. (a,b) ∈ I(R)}  I( ∃ R.C) = {a ∈ ∆ | exists b s.t. (a,b) ∈ I(R), b ∈ I(C)}  I(≥nR) = {a ∈ ∆ | |{b | (a, b) ∈ I(R)}| ≥ n}  I(≤nR) = {a ∈ ∆ | |{b | (a, b) ∈ I(R)}| ≤ n} 13 The SAME as in ClassL OVERVIEW :: SYNTAX :: SEMANTICS :: TBOX :: ABOX

 I( ∀ R.C) = {a ∈ ∆ | for all b, if (a,b) ∈ I(R) then b ∈ I(C)}  Those a that have only values b in C with role R. Interpretation of Value Restriction 14 b I(C) a if (a,b) ∈ I(R) b' b'' if (a,b') ∈ I(R) if (a,b'') ∈ I(R) OVERVIEW :: SYNTAX :: SEMANTICS :: TBOX :: ABOX

Interpretation of Existential Quantifier  I( ∃ R.C) = {a ∈ ∆ | exists b s.t. (a,b) ∈ I(R), b ∈ I(C)}  Those a that have some value b in C with role R. 15 b I(C) a (a,b) ∈ I(R) OVERVIEW :: SYNTAX :: SEMANTICS :: TBOX :: ABOX

Interpretation of Number Restriction  I(≥nR) = {a ∈ ∆ | |{b | (a, b) ∈ I(R)}|≥ n}  Those a that have relation R to at least n individuals. 16 ∆ a b b' … |{b | (a, b) ∈ I(R)}| ≥ n OVERVIEW :: SYNTAX :: SEMANTICS :: TBOX :: ABOX

Interpretation of Number Restriction Cont.  I(≤nR) = {a ∈ ∆ | |{b | (a, b) ∈ I(R)}| ≤n }  Those a that have relation R to at most n individuals. 17 ∆ a |{b | (a,b) ∈ I(R)}| ≤n b b' … OVERVIEW :: SYNTAX :: SEMANTICS :: TBOX :: ABOX

Terminology (TBox), same as in ClassL  A terminology (or TBox) is a set of definitions and specializations  Terminological axioms express constraints on the concepts of the language, i.e. they limit the possible models  The TBox is the set of all the constraints on the possible models 18 PhD ≡ Postgraduate ⊓ ≥3Publish Parent ≡ Person ⊓ ∃ hasChild.Person hasGrandChild ⊑ hasChild Equality axiom Definition Inclusion axiom Specialization TBOX Subsumption Equivalence OVERVIEW :: SYNTAX :: SEMANTICS :: TBOX :: ABOX

Reasoning with a TBox T, same as ClassL  Given two class-propositions P and Q, we want to reason about:  Satisfiability w.r.t. TT ⊨ P ? A concept P is satisfiable w.r.t. a terminology T, if there exists an interpretation I with I ⊨ θ for all θ ∈ T, and such that I ⊨ P, I(P)≠ ∅  Subsumption T ⊨ P ⊑ Q? T ⊨ Q ⊑ P? A concept P is subsumed by a concept Q w.r.t. T if I(P)  I(Q) for every model I of T  Equivalence T ⊨ P ⊑ Q and T ⊨ Q ⊑ P? Two concepts P and Q are equivalent w.r.t. T if I(P) = I(Q) for every model I of T  Disjointness T ⊨ P ⊓ Q ⊑ ⊥ ? Two concepts P and Q are disjoint with respect to T if their intersection is empty, I(P)  I(Q) = ∅, for every model I of T 19 OVERVIEW :: SYNTAX :: SEMANTICS :: TBOX :: ABOX

ABox, syntax  In an ABox one introduces individuals, by giving them names, and one asserts properties about them.  We denote individual names as a, b, c,…  An assertion with concept C is called concept assertion (or simply assertion) in the form: C(a), C(b), C(c), …  An assertion with Role R is called role assertion in the form: R(a, b), R(b, c), … 20 Student(paul) Professor(fausto) Teaches(Fausto, LDKR) OVERVIEW :: SYNTAX :: SEMANTICS :: TBOX :: ABOX

ABox, semantics  An interpretation I: L  pow(∆ I ) not only maps atomic concepts to sets, but in addition it maps each individual name a to an element a I ∈ ∆ I, namely I(a) = a I ∈ ∆ I I (C(a)) = a I ∈ C I, I(R(a, b)) = (a I, b I ) ∈ R I  Unique name assumption (UNA). We assume that distinct individual names denote distinct objects in the domain NOTE: ∆ I denotes the domain of interpretation, a denotes the symbol used for the individual (the name), while a I is the actual individual of the domain. 21 OVERVIEW :: SYNTAX :: SEMANTICS :: TBOX :: ABOX

Reasoning Services, same as ClassL  Given an ABox A, we can reason (w.r.t. a TBox T) about the following:  Satisfiability/Consistency: An ABox A is consistent with respect to T if there is an interpretation I which is a model of both A and T.  Instance checking: checking whether an assertion C(a) or R(a,b) is entailed by an ABox, i.e. checking whether a belongs to C. A ⊨ C(a) if every I that satisfies A also satisfies C(a). A ⊨ R(a,b) if every I that satisfies A also satisfies R(a,b).  Instance retrieval: given a concept C, retrieve all the instances a which satisfy C.  Concept realization: given a set of concepts and an individual a find the most specific concept(s) C (w.r.t. subsumption ordering) such that A ⊨ C(a). 22 OVERVIEW :: SYNTAX :: SEMANTICS :: TBOX :: ABOX