Principles of Knowledge Representation and...

Principles ofKnowledge Representation and ReasoningSemantic Networks and Description Logics II:Description Logics – Terminology and Notation

Albert-Ludwigs-Universität Freiburg

Bernhard Nebel, Stefan Wölfl, and Julien HuéJanuary 29, 2014

IntroductionMotivation

History

Systems andApplications

Description Logicsin a Nutshell

Conceptsand Roles

TBox andABox

ReasoningServices

Outlook

Literature

Appendix

Introduction

January 29, 2014 Nebel, Wölfl, Hué – KRR 2 / 36

History

Conceptsand Roles

TBox andABox

ReasoningServices

Outlook

Literature

Appendix

Motivation

Main problem with semantic networks and frames. . . the lack of formal semantics!Disadvantage of simple inheritance networks. . . concepts are atomic and do not have any structure

Brachman’s structural inheritance networks (1977)

History

Conceptsand Roles

TBox andABox

ReasoningServices

Outlook

Literature

Appendix

Structural inheritance networks

Concepts are defined/described using a small set ofwell-defined operatorsDistinction between conceptual and object-relatedknowledgeComputation of subconcept relation and of instance relationStrict inheritance (of the entire structure of a concept):inherited properties cannot be overriden

History

Conceptsand Roles

TBox andABox

ReasoningServices

Outlook

Literature

Appendix

Systems and applications

Systems:KL-ONE: First implementation of the ideas (1978)then: NIKL, KL-TWO, KRYPTON, KANDOR, CLASSIC,BACK, KRIS, YAK, CRACK . . .later: FaCT, DLP, RACER 1998currently: FaCT++, RACER, Pellet.

Applications:First, natural language understanding systems,then configuration systems,and information systems,currently, it is one tool for the Semantic Web

Languages: DAML+OIL, now OWL (Web OntologyLanguage)

History

Conceptsand Roles

TBox andABox

ReasoningServices

Outlook

Literature

Appendix

History

Conceptsand Roles

TBox andABox

ReasoningServices

Outlook

Literature

Appendix

History

Conceptsand Roles

TBox andABox

ReasoningServices

Outlook

Literature

Appendix

Description logics

Previously also known as KL-ONE-alike languages,frame-based languages, terminological logics, conceptlanguagesDescription Logics (DL) allow us

to describe concepts using complex descriptions,to introduce the terminology of an application and tostructure it (TBox),to introduce objects and relate them to the introducedterminology (ABox),and to reason about the terminology and the objects.

History

Conceptsand Roles

TBox andABox

ReasoningServices

Outlook

Literature

Appendix

Description logics

Previously also known as KL-ONE-alike languages,frame-based languages, terminological logics, conceptlanguagesDescription Logics (DL) allow us

to describe concepts using complex descriptions,to introduce the terminology of an application and tostructure it (TBox),to introduce objects and relate them to the introducedterminology (ABox),and to reason about the terminology and the objects.

History

Conceptsand Roles

TBox andABox

ReasoningServices

Outlook

Literature

Appendix

Informal example

Male is: the opposite of femaleA human is a kind of: living entityA woman is: a human and a femaleA man is: a human and a maleA mother is: a woman with at least one child that is a humanA father is: a man with at least one child that is a humanA parent is: a mother or a fatherA grandmother is: a woman, with at least one child that is a parentA mother-wod is: a mother with only male children

Elizabeth is a womanElizabeth has the childCharlesCharles is a manDiana is a mother-wodDiana has the child William

Possible Questions :Is a grandmother a parent?Is Diana a parent?Is William a man?Is Elizabeth a mother-wod?

History

Conceptsand Roles

TBox andABox

ReasoningServices

Outlook

Literature

Appendix

Informal example

Male is: the opposite of femaleA human is a kind of: living entityA woman is: a human and a femaleA man is: a human and a maleA mother is: a woman with at least one child that is a humanA father is: a man with at least one child that is a humanA parent is: a mother or a fatherA grandmother is: a woman, with at least one child that is a parentA mother-wod is: a mother with only male children

Elizabeth is a womanElizabeth has the childCharlesCharles is a manDiana is a mother-wodDiana has the child William

Possible Questions :Is a grandmother a parent?Is Diana a parent?Is William a man?Is Elizabeth a mother-wod?

Introduction

Conceptsand RolesConcept FormingOperators

Role FormingOperators

TBox andABox

ReasoningServices

Outlook

Literature

Appendix

Concepts and Roles

Introduction

TBox andABox

ReasoningServices

Outlook

Literature

Appendix

Atomic concepts and roles

Concept names:E.g., Grandmother, Male, . . . (in the following usuallycapitalized)We will use symbols such as A,A1, . . . for concept namesSemantics: Monadic predicates A(·) or set-theoretically asubset of the universe AI ⊆D.

Role names:In our example, e.g., child. Often we will use names suchas has-child or something similar (in the following usuallylowercase).Role names are disjoint from concept namesSymbolically: t, t1, . . .Semantics: Binary relations t(·, ·) or set-theoreticallytI ⊆D×D.

Introduction

TBox andABox

ReasoningServices

Outlook

Literature

Appendix

Atomic concepts and roles

Concept names:E.g., Grandmother, Male, . . . (in the following usuallycapitalized)We will use symbols such as A,A1, . . . for concept namesSemantics: Monadic predicates A(·) or set-theoretically asubset of the universe AI ⊆D.

Role names:In our example, e.g., child. Often we will use names suchas has-child or something similar (in the following usuallylowercase).Role names are disjoint from concept namesSymbolically: t, t1, . . .Semantics: Binary relations t(·, ·) or set-theoreticallytI ⊆D×D.

Introduction

TBox andABox

ReasoningServices

Outlook

Literature

Appendix

Concept and role description

From (atomic) concept and role names, complex conceptand role descriptions can be createdIn our example, e.g., “Human and Female.”Symbolically: C for concept descriptions and r for roledescriptions

Which particular constructs are available depends on the chosendescription logic!

FOL semantics: A concept description C corresponds to aformula C(x) with the free variable x.Similarly with role descriptions r: they correspond toformulae r(x,y) with free variables x,y.Set semantics:

CI = {d ∈ D : C(d) “is true in” I}rI =

{(d,e) ∈ D2 : r(d,e) “is true in” I

}January 29, 2014 Nebel, Wölfl, Hué – KRR 12 / 36

Introduction

TBox andABox

ReasoningServices

Outlook

Literature

Appendix

Introduction

TBox andABox

ReasoningServices

Outlook

Literature

Appendix

Introduction

TBox andABox

ReasoningServices

Outlook

Literature

Appendix

Boolean operators

Syntax: let C and D be concept descriptions, then thefollowing are also concept descriptions:

CuD (concept conjunction)CtD (concept disjunction)¬C (concept negation)

Examples:Human u FemaleFather t Mother¬ Female

FOL semantics: C(x)∧D(x), C(x)∨D(x), ¬C(x)Set semantics: CI ∩DI , CI ∪DI , D\CI

Introduction

TBox andABox

ReasoningServices

Outlook

Literature

Appendix

Boolean operators

Introduction

TBox andABox

ReasoningServices

Outlook

Literature

Appendix

Boolean operators

Introduction

TBox andABox

ReasoningServices

Outlook

Literature

Appendix

Boolean operators

Introduction

TBox andABox

ReasoningServices

Outlook

Literature

Appendix

Role restrictions

Motivation:Often we want to describe something by restricting thepossible “fillers” of a role, e.g. Mother-wod.Sometimes we want to say that there is at least a filler of aparticular type, e.g. Grandmother

Idea: Use quantifiers that range over the role-fillersMotheru∀has-child.ManWomanu∃has-child.Parent

FOL semantics:

(∃r.C)(x) = ∃y(r(x,y)∧C(y))(∀r.C)(x) = ∀y (r(x,y)→ C(y))

Set semantics:

(∃r.C)I ={

d ∈ D : there ex. some e s.t. (d,e) ∈ rI ∧e ∈ CI}(∀r.C)I =

{d ∈ D : for each e with (d,e) ∈ rI , e ∈ CI}

Introduction

TBox andABox

ReasoningServices

Outlook

Literature

Appendix

Role restrictions

FOL semantics:

Set semantics:

(∃r.C)I ={

Introduction

TBox andABox

ReasoningServices

Outlook

Literature

Appendix

Role restrictions

FOL semantics:

Set semantics:

(∃r.C)I ={

Introduction

TBox andABox

ReasoningServices

Outlook

Literature

Appendix

Cardinality restriction

Motivation:Often we want to describe something by restricting thenumber of possible “fillers” of a role, e.g., a Mother with atleast 3 children or at most 2 children.

Idea: We restrict the cardinality of the role filler sets:Motheru≥3has-childMotheru≤2has-child

FOL semantics:

(≥ n r)(x) = ∃y1 . . .yn(r(x,y1)∧·· ·∧ r(x,yn)∧y1 6= y2∧·· ·∧ yn−1 6= yn

)(≤ n r)(x) = ¬(≥ n+1 r)(x)

Set semantics:

(≥ n r)I ={

d ∈ D :∣∣{e ∈ D : rI(d,e)

}∣∣≥ n}

(≤ n r)I =D\ (≥ n+1 r)I

Introduction

TBox andABox

ReasoningServices

Outlook

Literature

Appendix

Cardinality restriction

Motivation:Often we want to describe something by restricting thenumber of possible “fillers” of a role, e.g., a Mother with atleast 3 children or at most 2 children.

Idea: We restrict the cardinality of the role filler sets:Motheru≥3has-childMotheru≤2has-child

FOL semantics:

(≥ n r)(x) = ∃y1 . . .yn(r(x,y1)∧·· ·∧ r(x,yn)∧y1 6= y2∧·· ·∧ yn−1 6= yn

)(≤ n r)(x) = ¬(≥ n+1 r)(x)

Set semantics:

(≥ n r)I ={

d ∈ D :∣∣{e ∈ D : rI(d,e)

}∣∣≥ n}

(≤ n r)I =D\ (≥ n+1 r)I

Introduction

TBox andABox

ReasoningServices

Outlook

Literature

Appendix

Inverse roles

Motivation:How can we describe the concept “children of rich parents”?

Idea: Define the “inverse” role for a given role (the converserelation)

has-child−1

Example: ∃has-child−1 .Rich

FOL semantics:

r−1(x,y) = r(y,x)

Set semantics:

(r−1)I ={(d,e) ∈ D2 : (e,d) ∈ rI

Introduction

TBox andABox

ReasoningServices

Outlook

Literature

Appendix

Inverse roles

Motivation:How can we describe the concept “children of rich parents”?

Idea: Define the “inverse” role for a given role (the converserelation)

has-child−1

Example: ∃has-child−1 .Rich

FOL semantics:

r−1(x,y) = r(y,x)

Set semantics:

(r−1)I ={(d,e) ∈ D2 : (e,d) ∈ rI

Introduction

TBox andABox

ReasoningServices

Outlook

Literature

Appendix

Role composition

Motivation:How can we define the role has-grandchild given the rolehas-child?

Idea: Compose roles (as one can compose binary relations)

has-child ◦ has-child

FOL semantics:

(r ◦ s)(x,y) = ∃z(r(x,z)∧ s(z,y))

Set semantics:

(r ◦ s)I ={(d,e) ∈ D2 : ∃f s.t. (d, f) ∈ rI ∧ (f ,e) ∈ sI

Introduction

TBox andABox

ReasoningServices

Outlook

Literature

Appendix

Role composition

Motivation:How can we define the role has-grandchild given the rolehas-child?

Idea: Compose roles (as one can compose binary relations)

has-child ◦ has-child

FOL semantics:

(r ◦ s)(x,y) = ∃z(r(x,z)∧ s(z,y))

Set semantics:

(r ◦ s)I ={(d,e) ∈ D2 : ∃f s.t. (d, f) ∈ rI ∧ (f ,e) ∈ sI

Introduction

TBox andABox

ReasoningServices

Outlook

Literature

Appendix

Role value maps

Motivation:How do we express the concept “women who know all thefriends of their children”

Idea: Relate role filler sets to each otherWoman u (has-child ◦ has-friend v knows)

FOL semantics:

(r v s)(x) = ∀y(r(x,y)→ s(x,y)

)Set semantics: Let rI(d) =

{e : rI(d,e)

(r v s)I ={

d ∈ D : rI(d)⊆ sI(d)}

Note: Role value maps lead to undecidability of satisfiabilitytesting of concept descriptions!

Introduction

Conceptsand Roles

TBox andABoxTerminology Box

Assertional Box

Example

ReasoningServices

Outlook

Literature

Appendix

TBox and ABox

Introduction

Conceptsand Roles

Assertional Box

Example

ReasoningServices

Outlook

Literature

Appendix

Terminology box

In order to introduce new terms, we use two kinds ofterminological axioms:

A .= C

Av Cwhere A is a concept name and C is a concept description.A terminology or TBox is a finite set of such axioms with thefollowing additional restrictions:

no multiple definitions of the same symbol such as A .= C,

Av Dno cyclic definitions (even not indirectly), such as A .

= ∀r .B,B .= ∃s .A

Introduction

Conceptsand Roles

Assertional Box

Example

ReasoningServices

Outlook

Literature

Appendix

Terminology box

In order to introduce new terms, we use two kinds ofterminological axioms:

A .= C

Av Cwhere A is a concept name and C is a concept description.A terminology or TBox is a finite set of such axioms with thefollowing additional restrictions:

no multiple definitions of the same symbol such as A .= C,

Av Dno cyclic definitions (even not indirectly), such as A .

= ∀r .B,B .= ∃s .A

Introduction

Conceptsand Roles

Assertional Box

Example

ReasoningServices

Outlook

Literature

Appendix

TBoxes: semantics

TBoxes restrict the set of possible interpretations.FOL semantics:

A .= C corresponds to ∀x

(A(x)↔ C(x)

)Av C corresponds to ∀x

(A(x)→ C(x)

)Set semantics:

A .= C corresponds to AI = CI

Av C corresponds to AI ⊆ CI

Non-empty interpretations which satisfy all terminologicalaxioms are called models of the TBox.

Introduction

Conceptsand Roles

Assertional Box

Example

ReasoningServices

Outlook

Literature

Appendix

TBoxes: semantics

(A(x)↔ C(x)

(A(x)→ C(x)

)Set semantics:

Introduction

Conceptsand Roles

Assertional Box

Example

ReasoningServices

Outlook

Literature

Appendix

TBoxes: semantics

(A(x)↔ C(x)

(A(x)→ C(x)

)Set semantics:

Introduction

Conceptsand Roles

Assertional Box

Example

ReasoningServices

Outlook

Literature

Appendix

Assertional box

In order to state something about objects in the world, weuse two forms of assertions:

a : C(a,b) : r

where a and b are individual names (e.g., ELIZABETH,PHILIP), C is a concept description, and r is a roledescription.

An ABox is a finite set of assertions.

Introduction

Conceptsand Roles

Assertional Box

Example

ReasoningServices

Outlook

Literature

Appendix

ABoxes: semantics

Individual names are interpreted as elements of theuniverse under the unique-name-assumption, i.e., differentnames refer to different objects.Assertions express that an object is an instance of aconcept or that two objects are related by a role.FOL semantics:

a : C corresponds to C(a)(a,b) : r corresponds to r(a,b)

Set semantics:aI ∈ Da : C corresponds to aI ∈ CI

(a,b) : r corresponds to (aI ,bI) ∈ rI

Models of an ABox and of ABox+TBox can be definedanalogously to models of a TBox.

Introduction

Conceptsand Roles

Assertional Box

Example

ReasoningServices

Outlook

Literature

Appendix

ABoxes: semantics

Introduction

Conceptsand Roles

Assertional Box

Example

ReasoningServices

Outlook

Literature

Appendix

ABoxes: semantics

Introduction

Conceptsand Roles

Assertional Box

Example

ReasoningServices

Outlook

Literature

Appendix

Example TBox

Male .= ¬Female

Human v Living_entityWoman .

= Human u FemaleMan .

= Human u MaleMother .

= Woman u ∃has-child.HumanFather .

= Man u ∃has-child.HumanParent .

= Father t MotherGrandmother .

= Woman u ∃has-child.ParentMother-without-daughter .

= Mother u ∀has-child.MaleMother-with-many-children .

= Mother u (≥ 3has-child)

Introduction

Conceptsand Roles

Assertional Box

Example

ReasoningServices

Outlook

Literature

Appendix

Example ABox

CHARLES: Man DIANA: WomanEDWARD: Man ELIZABETH: WomanANDREW: ManDIANA: Mother-without-daughter(ELIZABETH, CHARLES): has-child(ELIZABETH, EDWARD): has-child(ELIZABETH, ANDREW): has-child(DIANA, WILLIAM): has-child(CHARLES, WILLIAM): has-child

Introduction

Conceptsand Roles

TBox andABox

ReasoningServices

Outlook

Literature

Appendix

Reasoning Services

Introduction

Conceptsand Roles

TBox andABox

ReasoningServices

Outlook

Literature

Appendix

Some reasoning services

Does a description C make sense at all, i.e., is it satisfiable?A concept description C is satisfiable, if there exists aninterpretation I such that CI 6= /0.Is one concept a specialization of another one, is itsubsumed?C is subsumed by D (in symbols C v D) if we have for allinterpretations CI ⊆ DI .Is a an instance of a concept C?a is an instance of C if for all interpretations, we haveaI ∈ CI .Note: These questions can be posed with or without a TBoxthat restricts the possible interpretations.

Introduction

Conceptsand Roles

TBox andABox

ReasoningServices

Outlook

Literature

Appendix

Introduction

Conceptsand Roles

TBox andABox

ReasoningServices

Outlook

Literature

Appendix

Introduction

Conceptsand Roles

TBox andABox

ReasoningServices

Outlook

Literature

Appendix

Introduction

Conceptsand Roles

TBox andABox

ReasoningServices

Outlook

Literature

Appendix

Outlook

Introduction

Conceptsand Roles

TBox andABox

ReasoningServices

Outlook

Literature

Appendix

Outlook

Can we reduce the reasoning services to perhaps just oneproblem?What could be reasoning algorithms?What can we say about complexity and decidability?What has all that to do with modal logics?How can one build efficient systems?

Introduction

Conceptsand Roles

TBox andABox

ReasoningServices

Outlook

Literature

Appendix

Literature I

Baader, F., D. Calvanese, D. L. McGuinness, D. Nardi, and P. F.Patel-Schneider.The Description Logic Handbook: Theory, Implementation,Applications,Cambridge University Press, Cambridge, UK, 2003.

Ronald J. Brachman and James G. Schmolze.An overview of the KL-ONE knowledge representation system.Cognitive Science, 9(2):171–216, April 1985.

Franz Baader, Hans-Jürgen Bürckert, Jochen Heinsohn, BernhardHollunder, Jürgen Müller, Bernhard Nebel, Werner Nutt, and Hans-JürgenProfitlich.Terminological Knowledge Representation: A proposal for aterminological logic.Published in Proc. International Workshop on Terminological Logics,1991, DFKI Document D-91-13.

Introduction

Conceptsand Roles

TBox andABox

ReasoningServices

Outlook

Literature

Appendix

Literature II

Bernhard Nebel.Reasoning and Revision in Hybrid Representation Systems.Lecture Notes in Artificial Intelligence 422. Springer-Verlag, Berlin,Heidelberg, New York, 1990.

Introduction

Conceptsand Roles

TBox andABox

ReasoningServices

Outlook

Literature

Appendix

Summary: Concept descriptions

Abstract Concrete InterpretationA A AI

CuD (and C D) CI ∩DI

CtD (or C D) CI ∪DI

¬C (not C) D−CI

∀r.C (all r C){

d ∈D : rI(d)⊆ CI}∃r (some r)

{d ∈D : rI(d) 6= /0

}≥ n r (atleast n r)

{d ∈D : |rI(d)| ≥ n

}≤ n r (atmost n r)

{d ∈D : |rI(d)| ≤ n

}∃r.C (some r C)

{d ∈D : rI(d)∩CI 6= /0

}≥ n r.C (atleast n r C)

{d ∈D : |rI(d)∩CI | ≥ n

}≤ n r.C (atmost n r C)

{d ∈D : |rI(d)∩CI | ≤ n

}r ·= s (eq r s)

{d ∈D : rI(d) = sI(d)

}r 6= s (neq r s)

{d ∈D : rI(d) 6= sI(d)

}r v s (subset r s)

{d ∈D : rI(d)⊆ sI(d)

}g ·= h (eq g h)

{d ∈D : gI(d) = hI(d) 6= /0

}g 6= h (neq g h)

{d ∈D : /0 6= gI(d) 6= hI(d) 6= /0

}{i1, i2, . . . , in} (oneof i1 . . . in) {iI1 , i

I2 , . . . , i

Introduction

Conceptsand Roles

TBox andABox

ReasoningServices

Outlook

Literature

Appendix

Summary: Role descriptions

Abstract Concrete Interpretationt t tI

f f fI , (functional role)r u s (and r s) rI ∩ sI

r t s (or r s) rI ∪ sI

¬r (not r) D×D− rI

r−1 (inverse r){(d,d ′) : (d ′,d) ∈ rI

}r|C (restr r C)

{(d,d ′) ∈ rI : d ′ ∈ CI}

r+ (trans r) (rI)+

r ◦ s (compose r s) rI ◦ sI1 self {(d,d) : d ∈D}

Principles of Knowledge Representation and...

Documents

Paranchi elettrici TIPO KRR/3 a catena serie KRR Con ... · - Power supply 380-440 V / 50-60 Hz ... - Wheels profile suitable for flat or tapered beams ... t m m kW % 50 Hz m/min

foxPSL Poster at AAAI 2015 Spring Symposium on KRR

Voltaire - IBM Researchresearch.ibm.com/haifa//Workshops/SystemsAnd... · Voltaire High Performance InfiniBand Solutions Enabling High Performance Grid Computing Yaron Haviv CTO

Products for the Agriculture ndustry · RY2 KY2 KRRF KRR R S seal) KRRF KRR KY2 S is our source for a wide range of high ualit planter bearings for the following applications. Disc

Proximal humerus fractures by krr

Chapter 07: Monte Carlo Methods - uni-freiburg.deml.informatik.uni-freiburg.de/.../ws1314/gm/07-monte_carlo.handout.… · Markov chains Deﬁnitions: A Markov model or Markov chain

Cessna 206, VH-KRR - Australian Transport Safety … Final.pdf · Cessna 206, VH. Collision with terrain involving a -KRR ... preparations for a solo training flight from Bankstown,

The Implementation of Kafe-remaja Methode to Zero Triad-krr

Parts catalog and price list73-0394 104 #KRR-5X5X13 KEY (AX-M30, M60) 3 pc 73-0395 105 #KRR-5X5X15 KEY (AX-M12, M60) 1 pc 73-0394 106 #KRR-5X5X13 KEY (AX-M30, M60) 1 pc 73 …

KRR Lecture 1 Intro

260553 PPM00137EN Indhold€¦ · Leading in heat transfer technology Alfa Laval in brief AlfaLavalisaleadingglobalproviderof specializedproductsandengineered solutions.Ourequipment,systemsand

Knowledge Representation and Reasoningverbs.colorado.edu/~mpalmer/Ling7800/KRR-2013.pdf · Knowledge Representation and Reasoning Martha Palmer LING 7800/CSCI 7000 March 7, 2013 KRR

FastandAccurateGaussianKernelRidgeRegressionUsing … · 2020. 4. 27. · KRR, while simple, is a powerful technique that achieves empirically good results. However, KRR comes with

Support Systemsand Integrated Systems

Lymba (QA System)ad-teaching.informatik.uni-freiburg.de/information-extraction-ws1314… · Lymba – Fabian Schillinger 11/26 ROSE – a new NER System Uses 3-step process: Pass

GICTGreen ICT and Fibre Cabling Systemsand Fibre … Fibre Cabling Systemsand Fibre Cabling Systems ... tic LAN Section ... How FTTH can help the Ei tEnvironment

DISJOINTFRACTIONALPERMISSIONSIN …hobor/Teaching/LXB-PhD.pdf · DISJOINTFRACTIONALPERMISSIONSIN VERIFICATION:APPLICATIONS,SYSTEMSAND THEORY XUAN-BACHLE Bachelor in Computer Science

Ambush Marketing - sportrecht.orgsportrecht.org/cms/upload/seminararbeiten/WS1314/Geiss_Ambush... · Marketing“ oder „Guerilla Marketing“ verwendet, die in die gleiche Richtung

krrTitle krr Created Date 6/8/2019 10:22:31 AM

Open Information Extraction using Wikipediaad-teaching.informatik.uni-freiburg.de/information-extraction-ws1314… · Shortest Dependency Path as Relation •“Dan was not born in