Knowledge Representation andSemantic Web

Mario Alviano

University of Calabria, Italy

A.Y. 2017/2018

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Outline

1 Organization

2 Introduction

3 Fundamental questionsLanguagesLogic

4 Overview of the course

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Outline

1 Organization

2 Introduction

3 Fundamental questionsLanguagesLogic

4 Overview of the course

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About me

Mario AlvianoFirst and second degrees in Computer SciencePhD in Computer Science — Logic programming for AIFor details: http://www.alviano.net/

Consultation hourWednesday 10:30 – 11:30Check my website for changesYou may write me an e-mail to check if I will be in my office

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Course web page

https://www.mat.unical.it/ComputerScience/KnowledgeManagement

Hint

You can receive update messages via email1 Register yourself on the wiki

(unless you already did)2 Subscribe on the page

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Schedule

When?

Tuesday 17:00 – 19:00Wednesday 08:30 – 10:30Thursday 10:30 – 13:30

What?

Lectures and exercises, including PC exercises

Where?

Lab 31/a (here)

Check the web page for possible changes!

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Exams and attendance

Exams

Written, including PC exercisesDates to be fixedHomeworks presented in the class matter!(Up to around 3 bonus points on the first exam after thecourse)

Attendance

Attendance of the lectures is mandatoryTo access the exam you have to attend at least 70% of thecourse

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Exams and attendance

Exams

Written, including PC exercisesDates to be fixedHomeworks presented in the class matter!(Up to around 3 bonus points on the first exam after thecourse)

Attendance

Attendance of the lectures is mandatoryTo access the exam you have to attend at least 70% of thecourse

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Teaching material

Slides and material on the web page

https://www.mat.unical.it/ComputerScience/KnowledgeManagement

Suggested books

1 F. UriarteIntroduction to Knowledge Management

2 A. Asperti, A. CiabattoniLogica a Informatica

3 J. GallierLogic for Computer Science: Foundations of AutomaticTheorem Proving

4 D. Allemang, J. HendlerSemantic Web for the Working Ontologist

5 G. Antoniou, F. van HarmelenA Semantic Web Primer

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Outline

1 Organization

2 Introduction

3 Fundamental questionsLanguagesLogic

4 Overview of the course

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Why Semantic Web?

WWW history1980ies: Hypertext1990/1991: http://info.cern.ch1993: Mosaic Web Browser (later Netscape Navigator)1994: World Wide Web Consortium (W3C)1995: HTML 2.01997: HTML 3.21997: HTML 4.02000: HTML 4.01

Hyperlinked documents for humans are difficult to accessfor machinesSemantic Web: WWW also for machines

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Why Semantic Web?

WWW history1980ies: Hypertext1990/1991: http://info.cern.ch1993: Mosaic Web Browser (later Netscape Navigator)1994: World Wide Web Consortium (W3C)1995: HTML 2.01997: HTML 3.21997: HTML 4.02000: HTML 4.01

Hyperlinked documents for humans are difficult to accessfor machines

Semantic Web: WWW also for machines

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Why Semantic Web?

WWW history1980ies: Hypertext1990/1991: http://info.cern.ch1993: Mosaic Web Browser (later Netscape Navigator)1994: World Wide Web Consortium (W3C)1995: HTML 2.01997: HTML 3.21997: HTML 4.02000: HTML 4.01

Hyperlinked documents for humans are difficult to accessfor machinesSemantic Web: WWW also for machines

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Semantic Web Stack

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The route to the Semantic Web

The Semantic Web relies on Knowledge Representation andReasoning (KRR), the engineering part of KnowledgeManagement (KM).

Knowledge Management, a term from business

Identify, create, represent, distribute and enableadoption of insights, experiences, and practices.

Knowledge Management consists of1 Knowledge Acquisition2 Knowledge Representation3 Automated Reasoning

KRR = 2 + 3

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The route to the Semantic Web

The Semantic Web relies on Knowledge Representation andReasoning (KRR), the engineering part of KnowledgeManagement (KM).

Knowledge Management, a term from business

Identify, create, represent, distribute and enableadoption of insights, experiences, and practices.

Knowledge Management consists of1 Knowledge Acquisition2 Knowledge Representation3 Automated Reasoning

KRR = 2 + 3

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The route to the Semantic Web

The Semantic Web relies on Knowledge Representation andReasoning (KRR), the engineering part of KnowledgeManagement (KM).

Knowledge Management, a term from business

Identify, create, represent, distribute and enableadoption of insights, experiences, and practices.

Knowledge Management consists of1 Knowledge Acquisition2 Knowledge Representation3 Automated Reasoning

KRR = 2 + 3

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The route to the Semantic Web

The Semantic Web relies on Knowledge Representation andReasoning (KRR), the engineering part of KnowledgeManagement (KM).

Knowledge Management, a term from business

Identify, create, represent, distribute and enableadoption of insights, experiences, and practices.

Knowledge Management consists of1 Knowledge Acquisition2 Knowledge Representation3 Automated Reasoning

KRR = 2 + 3

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SECI model

Nonaka&Takeuchi

Tacit KnowledgeExplicit Knowledge

SocializationExternalizationCombinationInternalization

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Data, Information, Knowledge pyramid

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Data vs Information

745d51b8683b37806641074955a03d4e

md5sum of debian-7.2.0-amd64-xfce-CD-1.iso

Inca quipu

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Data vs Information

745d51b8683b37806641074955a03d4e

md5sum of debian-7.2.0-amd64-xfce-CD-1.iso

Inca quipu

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Data vs Information vs Knowledge

Pioneer plaque Voyager Golden Record

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Outline

1 Organization

2 Introduction

3 Fundamental questionsLanguagesLogic

4 Overview of the course

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Fundamental questions

Knowledge Representation and ReasoningDrawing conclusions from represented knowledge

How is the knowledge represented?How do we draw conclusions?

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Fundamental questions

Knowledge Representation and ReasoningDrawing conclusions from represented knowledgeHow is the knowledge represented?

How do we draw conclusions?

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Fundamental questions

Knowledge Representation and ReasoningDrawing conclusions from represented knowledgeHow is the knowledge represented?How do we draw conclusions?

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Outline

1 Organization

2 Introduction

3 Fundamental questionsLanguagesLogic

4 Overview of the course

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Languages

Which languages are suitable for Knowledge Representationand Reasoning?

Natural languages?Formal languages?

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Languages

Which languages are suitable for Knowledge Representationand Reasoning?

Natural languages?

Formal languages?

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Languages

Which languages are suitable for Knowledge Representationand Reasoning?

Natural languages?Formal languages?

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Natural languages (1)

It is raining.

If it is raining, the street is wet.

We can conclude thatThe street is wet.

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Natural languages (1)

It is raining.If it is raining, the street is wet.

We can conclude thatThe street is wet.

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Natural languages (1)

It is raining.If it is raining, the street is wet.

We can conclude thatThe street is wet.

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Natural languages (2)

ConsiderMario teaches the students in the classroom.

How to interpret it?

1 Mario teaches in the classroom.2 the students in the classroom.

All sorts of difficulties for automation!

Natural languages are not suitable forKnowledge Representation and Reasoning

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Natural languages (2)

ConsiderMario teaches the students in the classroom.

How to interpret it?1 Mario teaches in the classroom.

2 the students in the classroom.

All sorts of difficulties for automation!

Natural languages are not suitable forKnowledge Representation and Reasoning

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Natural languages (2)

ConsiderMario teaches the students in the classroom.

How to interpret it?1 Mario teaches in the classroom.2 the students in the classroom.

All sorts of difficulties for automation!

Natural languages are not suitable forKnowledge Representation and Reasoning

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Natural languages (2)

ConsiderMario teaches the students in the classroom.

How to interpret it?1 Mario teaches in the classroom.2 the students in the classroom.

All sorts of difficulties for automation!

Natural languages are not suitable forKnowledge Representation and Reasoning

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Formal languages

Knowledge Representation and Reasoning needsformal languages

with reasoning capabilities

Knowledge Representation and Reasoning needs(mathematical) logic!

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Formal languages

Knowledge Representation and Reasoning needsformal languageswith reasoning capabilities

Knowledge Representation and Reasoning needs(mathematical) logic!

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Formal languages

Knowledge Representation and Reasoning needsformal languageswith reasoning capabilities

Knowledge Representation and Reasoning needs(mathematical) logic!

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Outline

1 Organization

2 Introduction

3 Fundamental questionsLanguagesLogic

4 Overview of the course

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Mathematical logic

Julius Benedict (Arnold Schwarzenegger)[to the bad guy]:

You have no respect for logic.And I have no respect for thosewith no respect for logic.You’re a very stupid person.

[The bad guy gets beaten badly]

We conclude

If you don’t have respect for logic,you may get beaten up by ArnoldSchwarzenegger

How to draw such a conclusionautomatically?

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Mathematical logic

Julius Benedict (Arnold Schwarzenegger)[to the bad guy]:

You have no respect for logic.And I have no respect for thosewith no respect for logic.You’re a very stupid person.

[The bad guy gets beaten badly]

We conclude

If you don’t have respect for logic,you may get beaten up by ArnoldSchwarzenegger

How to draw such a conclusionautomatically?

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Mathematical logic

Julius Benedict (Arnold Schwarzenegger)[to the bad guy]:

You have no respect for logic.And I have no respect for thosewith no respect for logic.You’re a very stupid person.

[The bad guy gets beaten badly]

We conclude

If you don’t have respect for logic,you may get beaten up by ArnoldSchwarzenegger

How to draw such a conclusionautomatically?

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Automated Reasoning

Consider a sack of beans

1 2 3

1 All beans from the sack are green2 These are beans from the sack3 These are green beans

Deduction

1 + 2 ⇒ 3

Induction

2 + 3 ⇒ 1

Abduction

1 + 3 ⇒ 2

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Automated Reasoning

Consider a sack of beans

1 2 3

1 All beans from the sack are green2 These are beans from the sack3 These are green beans

Deduction

1 + 2 ⇒ 3

Induction

2 + 3 ⇒ 1

Abduction

1 + 3 ⇒ 2

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Automated Reasoning

Consider a sack of beans

1 2 3

1 All beans from the sack are green2 These are beans from the sack3 These are green beans

Deduction

1 + 2 ⇒ 3

Induction

2 + 3 ⇒ 1

Abduction

1 + 3 ⇒ 2

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Automated Reasoning

Consider a sack of beans

1 2 3

1 All beans from the sack are green2 These are beans from the sack3 These are green beans

Deduction

1 + 2 ⇒ 3

Induction

2 + 3 ⇒ 1

Abduction

1 + 3 ⇒ 2

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Automated Reasoning

Consider a sack of beans

1 2 3

1 All beans from the sack are green2 These are beans from the sack3 These are green beans

Deduction

1 + 2 ⇒ 3

Induction

2 + 3 ⇒ 1

Abduction

1 + 3 ⇒ 2

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Automated Reasoning

Consider a sack of beans

1 2 3

1 All beans from the sack are green2 These are beans from the sack3 These are green beans

Deduction

1 + 2 ⇒ 3

Induction

2 + 3 ⇒ 1

Abduction

1 + 3 ⇒ 2

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Automated Reasoning

Consider a sack of beans

1 2 3

1 All beans from the sack are green2 These are beans from the sack3 These are green beans

Deduction

1 + 2 ⇒ 3

Induction

2 + 3 ⇒ 1

Abduction

1 + 3 ⇒ 2

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Outline

1 Organization

2 Introduction

3 Fundamental questionsLanguagesLogic

4 Overview of the course

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Overview of the course

The remainder of the course will be more technical!

Description LogicsSemantic WebPropositional LogicFirst-Order Logic

Semantic Web

RDF, RDF-S and OWLXML and XML SchemaXPath and XSLT

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END OF THELECTURE

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