Introduction to Semantic Web Rules & Policies

1. Introduction to Semantic Web Rules & Policies Daniel Olmedilla L3s Research Center / Hannover University Programa de Postgrado en Ingeniería Informática y de Telecomunicación (Máster y Doctorado) Universidad Autónoma de Madrid, 3rd April, 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 adaptivity, lifelong learning and competence development • Intelligent systems/agents need to be guided • Software agents • Development is expensive • Are static • Are unflexible Universidad Autónoma de Madrid

3. About this lecture Objectives This lecture is intended to provide • reasons that motivated Semantic Web Research • 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 Universidad Autónoma de Madrid

4. About this lectureDisclaimer The objective is to present the main ideas not a full explanation of the theory that lays behind Universidad Autónoma de Madrid

5. About this lectureInteractive And also important • This is not • a conference presentation • a monologue • Each module partially builds on concepts from previous modules • Exercises are provided in order to strength understanding • You are also encouraged to interrupt andASK Questions whenever you need it Universidad Autónoma de Madrid

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 Lecture is available from: http://www.L3S.de/~olmedilla/presentations/2008/20080403_UAM_Master.ppt Universidad Autónoma de Madrid

7. OutlineLecture Overview Universidad Autónoma de Madrid

8. OutlineIntroduction Universidad Autónoma de Madrid

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 Universidad Autónoma de Madrid

10. IntroductionWhat is a policy? In a very broad way, a policy is defined as a statement defining the behaviour of an entity Universidad Autónoma de Madrid

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 ] Universidad Autónoma de Madrid

12. Exercise 1Specify your own policies How do you decide (in general terms) • which transportation you use to come to the university? • whether you share your • Homework? • 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? Universidad Autónoma de Madrid

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 Universidad Autónoma de Madrid

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  Universidad Autónoma de Madrid

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 Universidad Autónoma de Madrid

16. OutlineWhy the Semantic Web? Universidad Autónoma de Madrid

17. Why the Semantic Web? HTML: in your browser Universidad Autónoma de Madrid

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 Universidad Autónoma de Madrid

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 Universidad Autónoma de Madrid

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. Universidad Autónoma de Madrid

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 Universidad Autónoma de Madrid

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 Universidad Autónoma de Madrid

23. OutlineLast Year Lecture Universidad Autónoma de Madrid

24. Last Year LectureThe Semantic Web Stack XML / Namespaces URI / Unicode Universidad Autónoma de Madrid

25. XML / Namespaces URI / Unicode Last year lectureRDF foundations • Share basic syntax with other Web standards • URI: unique identifiers • Namespaces: organize/group identifiers • XML: reuse syntax and data types Universidad Autónoma de Madrid

26. XML / Namespaces URI / Unicode Last year lectureRDF Model • Data model facility • Evolution of hyperlinks • Open, extensible (open world assumption) • Graph model • Easy interconnection of distributed data Universidad Autónoma de Madrid

27. XML / Namespaces URI / Unicode Last year lectureRDF Schema • Facility for shared vocabulary • Properties to share link types • Classes to share resource types Universidad Autónoma de Madrid

28. XML / Namespaces URI / Unicode Last year lectureSPARQL • Querying facility • Flexible pattern matching • No reasoning • Some reasoning support via entailment regime Universidad Autónoma de Madrid

29. ontology complexity amount of data XML / Namespaces URI / Unicode Last year lectureOWL / Description Logic • Reasoning facility • Support complex ontology models • Reasoning on class and instance level Universidad Autónoma de Madrid

30. Last year lectureThe Semantic Web Stack Part of this year lecture XML / Namespaces URI / Unicode Universidad Autónoma de Madrid

31. Last year lectureWarning (or clarification ) • OWL: Web Ontology Language Ontology = OWL Universidad Autónoma de Madrid

32. OutlineRule-Based Representation & Reasoning Universidad Autónoma de Madrid

33. Rule-Based Representation and ReasoningWho uses logic? • Aristoteles • Spock • Mathematicians • Computer scientists • You Universidad Autónoma de Madrid

34. 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 • E.g., a SQL select statement specifies the properties of the data to be extracted from a DB, not the process of extracting the data • 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? Universidad Autónoma de Madrid

35. [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. Universidad Autónoma de Madrid

36. Rule-Based Representation and ReasoningRules are everywhere (II) Declarative Universidad Autónoma de Madrid

37. Rule-Based Representation and ReasoningRules are everywhere (III) Universidad Autónoma de Madrid

38. 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 Universidad Autónoma de Madrid

39. 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. Universidad Autónoma de Madrid

40. Rule-Based Representation and ReasoningSyntax of First Order Predicate Logic (FOL) Logical Symbols • punctuation, connectives, quantifiers, variables Signature Symbols • n-ary function symbols (0-ary = constant) • n-ary relation symbols Term x Mary founder (x) founder (Web5.0) Atom married(Mary ,Tom) married(founder (y),Tom) Formula person(Mary )  person(Tom)  company(Web5.0)  x (company(x)  person(founder (x) ) Universidad Autónoma de Madrid

41. Rule-Based Representation and ReasoningStandard Notions (FOL) Bound/free􀀀 ( x [x p(x)  q(x) ]  [ r (x)  x s(x)] ) Closed • no free variables Ground • no variables Propositional􀀀 [p  q]  [ r  s] Universidad Autónoma de Madrid

42. 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 Universidad Autónoma de Madrid

43. Rule-Based Representation and ReasoningLogic Programming Literal atom A, negated atom A Clause A1 …  Ak← L1 … Ln atoms Ai , literals Lj , k ≥ 0, n ≥ 0 TENCompetence WS Universidad Autónoma de Madrid Feb. 21st, 2008 43

44. 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? Universidad Autónoma de Madrid

45. Rule-Based Representation and ReasoningPossibility 1: Enumerate all the facts Try to enumerate all that information for your agent: Universidad Autónoma de Madrid

46. 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 Universidad Autónoma de Madrid

47. 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) . Universidad Autónoma de Madrid

48. 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’). Universidad Autónoma de Madrid

49. Rule-Based Representation and ReasoningAdvantages • Declarative • Infer implicit knowledge • Compact representation • Well-defined semantics • Available proofs • Truths that it establishes are absolute Universidad Autónoma de Madrid

50. 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 Universidad Autónoma de Madrid