Knowledge Representation and Semantic Web

Transcription

1 1 / 29 Knowledge Representation and Semantic Web Mario Alviano University of Calabria, Italy A.Y. 2017/2018

2 Outline 2 / 29 1 Organization 2 Introduction 3 Fundamental questions Languages Logic 4 Overview of the course

3 Outline 3 / 29 1 Organization 2 Introduction 3 Fundamental questions Languages Logic 4 Overview of the course

4 About me 4 / 29 Mario Alviano First and second degrees in Computer Science PhD in Computer Science Logic programming for AI For details: Consultation hour Wednesday 10:30 11:30 Check my website for changes You may write me an to check if I will be in my office

5 Course web page 5 / 29 KnowledgeManagement Hint You can receive update messages via 1 Register yourself on the wiki (unless you already did) 2 Subscribe on the page

6 6 / 29 Schedule When? Tuesday 17:00 19:00 Wednesday 08:30 10:30 Thursday 10:30 13:30 What? Lectures and exercises, including PC exercises Where? Lab 31/a (here) Check the web page for possible changes!

7 7 / 29 Exams and attendance Exams Written, including PC exercises Dates to be fixed Homeworks presented in the class matter! (Up to around 3 bonus points on the first exam after the course)

8 7 / 29 Exams and attendance Exams Written, including PC exercises Dates to be fixed Homeworks presented in the class matter! (Up to around 3 bonus points on the first exam after the course) Attendance Attendance of the lectures is mandatory To access the exam you have to attend at least 70% of the course

9 Teaching material Slides and material on the web page Suggested books 1 F. Uriarte Introduction to Knowledge Management 2 A. Asperti, A. Ciabattoni Logica a Informatica 3 J. Gallier Logic for Computer Science: Foundations of Automatic Theorem Proving 4 D. Allemang, J. Hendler Semantic Web for the Working Ontologist 5 G. Antoniou, F. van Harmelen A Semantic Web Primer 8 / 29

10 Outline 9 / 29 1 Organization 2 Introduction 3 Fundamental questions Languages Logic 4 Overview of the course

11 Why Semantic Web? 10 / 29 WWW history 1980ies: Hypertext 1990/1991: : Mosaic Web Browser (later Netscape Navigator) 1994: World Wide Web Consortium (W3C) 1995: HTML : HTML : HTML : HTML 4.01

12 10 / 29 Why Semantic Web? WWW history 1980ies: Hypertext 1990/1991: : Mosaic Web Browser (later Netscape Navigator) 1994: World Wide Web Consortium (W3C) 1995: HTML : HTML : HTML : HTML 4.01 Hyperlinked documents for humans are difficult to access for machines

13 10 / 29 Why Semantic Web? WWW history 1980ies: Hypertext 1990/1991: : Mosaic Web Browser (later Netscape Navigator) 1994: World Wide Web Consortium (W3C) 1995: HTML : HTML : HTML : HTML 4.01 Hyperlinked documents for humans are difficult to access for machines Semantic Web: WWW also for machines

14 Semantic Web Stack 11 / 29

15 The route to the Semantic Web 12 / 29 The Semantic Web relies on Knowledge Representation and Reasoning (KRR), the engineering part of Knowledge Management (KM).

16 The route to the Semantic Web 12 / 29 The Semantic Web relies on Knowledge Representation and Reasoning (KRR), the engineering part of Knowledge Management (KM). Knowledge Management, a term from business Identify, create, represent, distribute and enable adoption of insights, experiences, and practices.

17 12 / 29 The route to the Semantic Web The Semantic Web relies on Knowledge Representation and Reasoning (KRR), the engineering part of Knowledge Management (KM). Knowledge Management, a term from business Identify, create, represent, distribute and enable adoption of insights, experiences, and practices. Knowledge Management consists of 1 Knowledge Acquisition 2 Knowledge Representation 3 Automated Reasoning

18 12 / 29 The route to the Semantic Web The Semantic Web relies on Knowledge Representation and Reasoning (KRR), the engineering part of Knowledge Management (KM). Knowledge Management, a term from business Identify, create, represent, distribute and enable adoption of insights, experiences, and practices. Knowledge Management consists of 1 Knowledge Acquisition 2 Knowledge Representation 3 Automated Reasoning KRR = 2 + 3

19 13 / 29 SECI model Nonaka&Takeuchi Tacit Knowledge Explicit Knowledge Socialization Externalization Combination Internalization

20 Data, Information, Knowledge pyramid 14 / 29

21 Data vs Information 15 / d51b8683b a03d4e

22 Data vs Information 15 / d51b8683b a03d4e md5sum of debian amd64-xfce-cd-1.iso Inca quipu

23 Data vs Information vs Knowledge 16 / 29 Pioneer plaque Voyager Golden Record

24 Outline 17 / 29 1 Organization 2 Introduction 3 Fundamental questions Languages Logic 4 Overview of the course

25 18 / 29 Fundamental questions Knowledge Representation and Reasoning Drawing conclusions from represented knowledge

26 18 / 29 Fundamental questions Knowledge Representation and Reasoning Drawing conclusions from represented knowledge How is the knowledge represented?

27 18 / 29 Fundamental questions Knowledge Representation and Reasoning Drawing conclusions from represented knowledge How is the knowledge represented? How do we draw conclusions?

28 Outline 19 / 29 1 Organization 2 Introduction 3 Fundamental questions Languages Logic 4 Overview of the course

29 Languages 20 / 29 Which languages are suitable for Knowledge Representation and Reasoning?

30 Languages 20 / 29 Which languages are suitable for Knowledge Representation and Reasoning? Natural languages?

31 20 / 29 Languages Which languages are suitable for Knowledge Representation and Reasoning? Natural languages? Formal languages?

32 Natural languages (1) 21 / 29 It is raining.

33 21 / 29 Natural languages (1) It is raining. If it is raining, the street is wet.

34 21 / 29 Natural languages (1) It is raining. If it is raining, the street is wet. We can conclude that The street is wet.

35 22 / 29 Natural languages (2) Consider Mario teaches the students in the classroom. How to interpret it?

36 22 / 29 Natural languages (2) Consider Mario teaches the students in the classroom. How to interpret it? 1 Mario teaches in the classroom.

37 22 / 29 Natural languages (2) Consider Mario teaches the students in the classroom. How to interpret it? 1 Mario teaches in the classroom. 2 the students in the classroom.

38 22 / 29 Natural languages (2) Consider Mario 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 for Knowledge Representation and Reasoning

39 23 / 29 Formal languages Knowledge Representation and Reasoning needs formal languages

40 23 / 29 Formal languages Knowledge Representation and Reasoning needs formal languages with reasoning capabilities

41 23 / 29 Formal languages Knowledge Representation and Reasoning needs formal languages with reasoning capabilities Knowledge Representation and Reasoning needs (mathematical) logic!

42 Outline 24 / 29 1 Organization 2 Introduction 3 Fundamental questions Languages Logic 4 Overview of the course

43 Mathematical logic 25 / 29 Julius Benedict (Arnold Schwarzenegger) [to the bad guy]: You have no respect for logic. And I have no respect for those with no respect for logic. You re a very stupid person. [The bad guy gets beaten badly]

44 Mathematical logic 25 / 29 Julius Benedict (Arnold Schwarzenegger) [to the bad guy]: You have no respect for logic. And I have no respect for those with 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 Arnold Schwarzenegger

45 Mathematical logic 25 / 29 Julius Benedict (Arnold Schwarzenegger) [to the bad guy]: You have no respect for logic. And I have no respect for those with 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 Arnold Schwarzenegger How to draw such a conclusion automatically?

46 Automated Reasoning Consider a sack of beans All beans from the sack are green 2 These are beans from the sack 3 These are green beans 26 / 29

47 Automated Reasoning Consider a sack of beans All beans from the sack are green 2 These are beans from the sack 3 These are green beans / 29

48 Automated Reasoning Consider a sack of beans All beans from the sack are green 2 These are beans from the sack 3 These are green beans Deduction / 29

49 Automated Reasoning Consider a sack of beans All beans from the sack are green 2 These are beans from the sack 3 These are green beans Deduction / 29

50 Automated Reasoning Consider a sack of beans All beans from the sack are green 2 These are beans from the sack 3 These are green beans Deduction Induction / 29

51 Automated Reasoning Consider a sack of beans All beans from the sack are green 2 These are beans from the sack 3 These are green beans Deduction Induction / 29

52 Automated Reasoning Consider a sack of beans All beans from the sack are green 2 These are beans from the sack 3 These are green beans Deduction Induction Abduction / 29

53 Outline 27 / 29 1 Organization 2 Introduction 3 Fundamental questions Languages Logic 4 Overview of the course

54 28 / 29 Overview of the course The remainder of the course will be more technical! Description Logics Semantic Web Propositional Logic First-Order Logic Semantic Web RDF, RDF-S and OWL XML and XML Schema XPath and XSLT

55 END OF THE LECTURE 29 / 29