Introduction to Semantic Web Rules & Policies

1. Introduction to Semantic Web Rules & Policies Daniel Olmedilla, Philipp Kärger L3s Research Center / Hannover University TENCompetence Winter School Innsbruck, 21st February 2008

2. About this lectureWhy this lecture? • Lot of noise about the Semantic Web • Lot of relevant papers and work on Semantic Web in last years • Techniques and tools can be used in the context of lifelong learning and competence development • Intelligent systems/agents need to be guided • Software agents • Development is expensive • Are static • Are unflexible TENCompetence WS

3. About this lecture Objectives This lecture is intended to provide • reasons that motivated Semantic Web Research (revisited) • a basic understanding of rule-based representation • a basic introduction to reasoning techniques • a basic understanding of requirements of current distributed systems • a motivation for the use of policies • a basic introduction to rule-based policies and their applications • a basic introduction to reactive policies TENCompetence WS

4. About this lectureDisclaimer The objective is to present the main ideas not an explanation of the theory that lays behind TENCompetence WS

5. About this lectureInteractive And also important • This is not • a conference presentation • a monologue • Each module partially builds on concepts from previous modules • We provide exercises to strength understanding • You are also encouraged to interrupt andASK Questions whenever you need it TENCompetence WS

6. About this lectureThe slides Slides are wordy so they can be easily understood offline after the tutorial More definitions and references are available in notes and hidden slides Tutorial is available from: http://www.L3S.de/~olmedilla/events/2008/TENC-WS-SWP/20080221_TENC_WS.ppt TENCompetence WS

7. OutlineLecture Overview TENCompetence WS

8. OutlineIntroduction TENCompetence WS

9. IntroductionWarming Up: Problem Institutions, companies and people need to control the way they • Make business • Take decisions • Offer their assets • Etc … Computers help us on our daily work performing tasks • that we cannot perform (or we do it worse) • automatically on our behalf But generally, we need to control how decisions and actions are taken TENCompetence WS

10. IntroductionWhat is a policy? In a very broad way, a policy is defined as a statement defining the behaviour of an entity TENCompetence WS

11. IntroductionPolicies are everywhere • B2B contracts • e.g. quantity flexible contracts, late delivery penalties, etc. • Negotiation • e.g. rules associated with auction mechanisms • Security • e.g. access control policies • Privacy • Information Collection Policies (aka “ P3P Privacy Policies”) • Obfuscation Policies • Workflow management • What to do under different sets of conditions • Context aware computing • What service to invoke to access a particular contextual attribute • Context-sensitive preferences [ by Norman Sadeh, Semantic Web Policy Workshop panel,ISWC 2005 ] TENCompetence WS

12. Exercise 1Specify your own policies How do you decide (in general terms) • which transportation you use to come to this event? • whether you share your • PhD thesis draft? • Pictures from your holidays in Hawaii? • Your famous report so many companies are willing to pay for? • whether you take a private call when being at work? • which tasks you perform everyday at work? TENCompetence WS

13. Exercise 1Problem (I) Now imagine a system application or software agent could/should decide on your behalf. How do you tell such an agent how it should do it? The way we make business, take decisions, etc. • Is dynamic, that is, often changes • Evolves with the time  We cannot re-code, re-compile, re-install a new software agent every time we change the way we take decisions TENCompetence WS

14. Exercise 1Problem (II) Furthermore, we need that the system acting on our behalf • does what we want • How do we tell it? • What if we make a mistake and tell something wrong? • is contextual, that is, depends on many factors • is “intelligent” (does things as we would do them) • is not reserved only to millionaires TENCompetence WS

15. IntroductionThe goal Build applications/agents where • Behaviour is flexible • Can be changed/updated • without re-coding, re-compiling, re-installing, etc… • In a costless manner • Can be managed by administrators/users without needing to be computer experts • Can be understood by normal users TENCompetence WS

16. OutlineWhy the Semantic Web? TENCompetence WS

17. Why the Semantic Web? HTML: in your browser TENCompetence WS

18. Why the Semantic Web? HTML: Markup <h2> Topics </h2><p>Educational Principles <br/>Knowledge Management <br/>Education Process Modeling <br/>Learning Design <br/>Competence Development <br/>…</p><h2> Lecturers </h2><p>Albert Angehrn, INSEAD, France <br/>Boyan Bontchev, Sofia University, Bulgaria <br/>Alexandar Dimov, Sofia University, Bulgaria <br/>Dai Griffiths, University of Bolton, United Kingdom <br/>…</p> Markup for presentation only TENCompetence WS

19. Why the Semantic Web? HTML: Limitations HTML deals only with formatting of data It does not provide information about the data it contains Query engines do a great job but queries like • Give me the list of subjects that the winter school will deal with • Return the affiliations of the lecturers in the winter school are not possible on the current Web Search on current Web is based on syntactic matching TENCompetence WS

20. [Eric Miller. Weaving Meaning : An Overview of The Semantic Web. 2003 ] Why the Semantic Web? Current Web • Downloadable Resources: • identified by URL's • untyped • Links: • href, src, ... • limited, non-descriptive • User: • Exciting world • semantics of the resource, however, gleaned from content • Machine processable: • Very little information available • significance of the links only evident from the context around the anchor. TENCompetence WS

21. Why the Semantic Web? Semantic Web Definition “The Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation.” Tim Berners-Lee, James Hendler, Ora LassilaThe Semantic Web, Scientific American, May 17, 2001 TENCompetence WS

22. [Eric Miller. Weaving Meaning : An Overview of The Semantic Web. 2003 ] Why the Semantic Web? The Semantic Web • Resources (any resource): • Globally Identified by URI's • Extensible • Relational • Links: • Identified by URI's • Extensible • Relational • User: • Even more exciting world, richer user experience • Machine: • More processable information is available (Data Web) • Computers and people: • Work, learn and exchange knowledge effectively TENCompetence WS

23. OutlineLast Year Lecture TENCompetence WS

24. Last year lectureWarning (or clarification ) OWL: Web Ontology Language Ontology = OWL TENCompetence WS

25. Last year lectureIntroduction to Semantic Web TENCompetence WS

26. Last year lectureThe Semantic Web Stack Part of this year lecture Last year lecture XML / Namespaces URI / Unicode TENCompetence WS

27. OutlineRule-Based Representation & Reasoning TENCompetence WS

28. Rule-Based Representation and ReasoningWho uses logic? • Aristoteles • Spock • Mathematicians • Computer scientists • You TENCompetence WS

29. Exercise 1Revisited (I) Were your policies • declarative? • That is, they specify the what (conditions) but not the how (algorithm or process to satisfy them) • E.g., HTML pages describe what the page should contain but not how to actually display the page on a computer screen • using inference rules? • E.g., If destination is in Europe then max price is … • E.g., If distance is less than … then go by train • if not, do you think they are more naturally modelled as rules? TENCompetence WS

30. [Isaac Asimov. Runaround. 1942 ] Rule-Based Representation and ReasoningRules are everywhere (I) Rules of ethics for robots • A robot may not injure a human being or, through inaction, allow a human being to come to harm. • A robot must obey orders given to it by human beings, except where such orders would conflict with the First Law. • A robot must protect its own existence as long as such protection does not conflict with the First or Second Law. TENCompetence WS

31. Rule-Based Representation and ReasoningRules are everywhere (II) Declarative TENCompetence WS

32. Rule-Based Representation and ReasoningRules are everywhere (III) TENCompetence WS

33. Rule-Based Representation and ReasoningInference Rule (I) Relation holding between premises (antecedent) and conclusions (consequent) The conclusion is said to be inferable (or derivable or deducible) from the premises We can infer new knowledge TENCompetence WS

34. Rule-Based Representation and ReasoningInference Rule (II) Rule notation: consequent ← antecedent Stands for antecedent  consequent that is, IF antecedent THEN consequent Examples: • If someone is a man then he is mortal mortal(X) ← man(X). • If someone is in this lecture, then he/she is a researcher researcher(X) ← inThisLecture(X). It does not matter what X is, the rule is always valid. Base for deductive reasoning TENCompetence WS

35. Rule-Based Representation and ReasoningDeductive vs. Inductive Reasoning Deductive:proceeds from general principles or premises to derive particular information (conclusions). Example • All apples are fruit. • All fruits grow on trees. • Therefore all apples grow on trees. Remember Sherlock Holmes? Inductive: the premises of an argument are believed to support the conclusion but do not ensure its truth. • Makes generalizations (from empirical observations) Example • All observed crows are black. • Therefore all crows are black. TENCompetence WS

36. Rule-Based Representation and ReasoningExample: information about your family Assume an agent needs to know all the information about your closest relatives. How do you inform your agent about such information? TENCompetence WS

37. Rule-Based Representation and ReasoningPossibility 1: Enumerate all the facts Try to enumerate all that information for your agent: TENCompetence WS

38. Rule-Based Representation and ReasoningPossibility 2: facts + rules + deduction Axioms/Facts • Tom is the father of Mary father(‘Tom’,’Mary’). • Alice is the sister of Mary sister(‘Alice’,’Mary’). • Clara is the sister of Mary sister(‘Clara’,’Mary’). • Mary is the mother of Anne mother(‘Mary’,‘Anne’). • Clara is the mother of Bob mother(‘Clara’,‘Bob’). • A parent is either a father or a mother parent(P,C) ← father(P,C)  mother(P,C). • The parent of your sister is your parent parent(P,C) ← parent(P,X)  sister(X,C) . • The parent of a parent is a grandparent grandparent(P,C) ← parent(P,X)  parent(X,C). • An aunt is the sister of a parent aunt(A,C) ← sister(A,X)  parent(X,C) . Inference Rules TENCompetence WS

39. Rule-Based Representation and ReasoningExercise 2: deductive reasoning Given such a program, write down the inferred new knowledge • Tom is the father of Mary father(‘Tom’,’Mary’). • Alice is the sister of Mary sister(‘Alice’,’Mary’). • Clara is the sister of Mary sister(‘Clara’,’Mary’). • Mary is the mother of Anne mother(‘Mary’,‘Anne’). • Clara is the mother of Bob mother(‘Clara’,‘Bob’). • A parent is either a father or a mother parent(P,C) ← father(P,C)  mother(P,C). • The parent of your sister is your parent parent(P,C) ← parent(P,X)  sister(X,C) . • The parent of a parent is a grandparent grandparent(P,C) ← parent(P,X)  parent(X,C). • An aunt is the sister of a parent aunt(A,C) ← sister(A,X)  parent(X,C) . TENCompetence WS

40. Rule-Based Representation and ReasoningExercise 2: solution Given such a program, write down the inferred new knowledge From first rule: • Tom is the parent of Mary parent(‘Tom’,’Mary’). • Mary is the parent of Anne parent(‘Mary’,’Anne’). • Clara is the parent of Bob parent(‘Clara’,’Bob’). From second rule (+ the first rule): • Tom is the parent of Alice parent(‘Tom’,’Alice’). • Tom is the parent of Clara parent(‘Tom’,’Clara’). From the third rule (+ the first and second) • Tom is the grandparent of Anne grandparent(‘Tom’,’Anne’). • Tom is the grandparent of Bob grandparent(‘Tom’,’Bob’). From the forth rule (+ the first rule) • Alice is the aunt of Anne aunt(‘Alice’,’Anne’). • Clara is the aunt of Anne aunt(‘Clara’,’Anne’). • Mary is the aunt of Bob aunt(‘Mary’,’Bob’). • Alice is the aunt of Bob aunt(‘Alice’,’Bob’). TENCompetence WS

41. Rule-Based Representation and ReasoningAdvantages • Declarative • Infer implicit knowledge • Compact representation • Well-defined semantics • Available proofs • Truths that it establishes are absolute TENCompetence WS

42. Rule-Based Representation and ReasoningDisadvantages • Wrongly specified rules  wrong implicit knowledge • It must have some truths in hand before starting • Sometimes you don’t have them all • Sometimes not all is true or false • You need to specify all right rules • Otherwise, underspecified programs TENCompetence WS

43. OutlineSemantic Web Policies TENCompetence WS

44. Semantic Web PoliciesWhat is a policy? Definitions • A statement defining the behaviour of an entity • An enforceable, well-specified constraint on the performance of a machine-executable action by a subject in a given situation • A deliberate plan of action to guide decisions and achieve rational outcome(s). TENCompetence WS

45. Semantic Web PoliciesA broader notion of policy The term policy covers: • Security/Privacy policies, Trust management • Business rules • Quality of Service directives • Service-level agreements • Communication and conversation policies • and more... TENCompetence WS

46. Semantic Web PoliciesAn e-learning scenario (I) Exploiting agents to support collaborative learning in an on-line learning community: They offer means to handle this complex setting as we will learn from the following four scenarios TENCompetence WS

47. Semantic Web PoliciesAn e-learning scenario (II) “Only my tutor is able to access myhomework. My fellow students are able to access my lecture notes but not my homework.” • Access control • Security • Trust management TENCompetence WS

48. Semantic Web PoliciesAn e-learning scenario (III) “I want to be reminded two days before my homework is due.” “I want to get an SMS if my tutor extends a homework’s deadline.”  Reactive Agents • Events (e.g., deadline extension) trigger agent decisions TENCompetence WS

49. Semantic Web PoliciesAn e-learning scenario (IV) “While using my e-learning tool I only want to receive chat messages from my fellow students and my tutor. Others get an automatic reply ‘Please contact me later, I am busy’.”  Communication Control TENCompetence WS

50. Semantic Web PoliciesAn e-learning scenario (V) “In order to purchase learning material I use my Credit Card only with parties providing the ‘Online Security Certificate’.” • Agent Negotiations • Privacy Step 4 Step 3 Step 2 Step 1 TENCompetence WS