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LDK R Logics for Data and Knowledge Representation Exercise 3: DLs.

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Presentation on theme: "LDK R Logics for Data and Knowledge Representation Exercise 3: DLs."— Presentation transcript:

1 LDK R Logics for Data and Knowledge Representation Exercise 3: DLs

2 Outline  Modeling  Previous Logics  DL  RelBAC  OWL  Comprehensive 2

3 Modeling Procedure  Abstraction of the world to a mental model  Clarify the domain of interest  Clarify the relations  Choose/build a logic  Build the theory of the mental model with the logic  Reason about the theory 3 <!DOCTYPE rdf:RDF[… ]> <rdf:RDF… > …

4 What distincts DL from Previous Logics?  PL  Logical constructors  Interpretations  ClassL  Logical constructors  Interpretations  Ground ClassL  Individuals  DL  All? 4

5 Summary of Previous Logics We Mentioned 5 PLClassLGround ClassLDL Syn. NpNcNc, NiNc, Ni, Nr ∧⊓⊓⊓* ∨⊔⊔⊔*  ** ⊤⊤⊤⊤ ⊥⊥⊥⊥ →⊑⊑⊑* ↔≡≡≡* Set Fill ∃∀ ≤≥ Sem.∆={true, false}∆={e1, …}

6 Expressiveness of DL  Binary Relations? YES!  Subsumption? Of course! More than concept subsumption! Arbitrary!  Else?  Some  Only  Number 6

7 Description Logics  Propositional DL VS. ClassL  DL VS. Ground ClassL  Role Constructors ∃∃ ∀∀ ≤≤ ≥≥  ⊓⊔¬≡⊑ 7 In addition to concept constructors

8 DL Modeling Model the following NL sentences with DLs.  “Children with only a single parent and no siblings” Child ⊓≤1 hasParent ⊓≥1 hasparent ⊓∀ hasSibling. ⊥  “Friends that likes foreign movies but only Disney cartoons” Friend ⊓∃ like.(Movie ⊓ Foreign) ⊓∀ like.(Cartoon ⊓ Disney)  “A binary tree is a tree with at most two sub-trees that are themselves binary trees.” BTree ≡ Tree ⊓≤2 hasSubTree.BTree  “The monkeys that can grasp the banana are those that have climbed onto the box at position of the banana” Monkey ⊓∃ get.Banana ⊑∃ hasClimbedOnto.(Box ⊓∃ atPo sitionOf.Banana) 8

9 DL Reasoning: TBox  Prove the following tautology: ¬(C ⊓ D)≡¬C ⊔ ¬D¬ ∀ R.C≡ ∃ R.¬C  Venn Diagrams  Concepts: Universal, Arbitrary non-empty set, Empty set  Relations: Intersection, union, disjoint  Tableaux  An algorithm to verify satisfiability.  Rules: and/or/some/only 9

10 ¬ (C ⊓ D) ≡¬ C ⊔¬ D 10 C D C D C D C D CD C D CD

11 ¬∀ R.C ≡∃ R. ¬ C (¬∀ R.C) I = Δ -{x| ∀ y R(x,y)→C(y) } = {x| ¬(∀ y R(x,y)→C(y) )} ={x| ∃ y ¬( R(x,y)→C(y) )} ={x| ∃ y ¬(¬ R(x,y) ∨ C(y) )} ={x| ∃ y R(x,y) ∧¬ C(y) )} =( ∃ R. ¬ C) I 11

12 DL Reasoning: ABox (1)  Given the interpretation I with the domain Δ I ={d,e,f,g} {d,e,f} ⊑ AB(f)R(d,e)R(e,g) S(g,d)S(g,g)S(e,f) In which A,B are concept and R,S are roles.  Please find the instances of the ALC-concept C as  A ⊔ B  ∃ S. ¬ A  ∀ S.A  ∃ S. ∃ S. ∃ S. ∃ S.A  ∀ T.A ⊓∀ T. ¬ A 12 e g d f A AA,B RR S S S

13 DL Reasoning: ABox (2)  Let an ABox A consists of the following assertions: Likes(Bob, Ann)Likes(Bob, Cate) Neighbor(Ann, Cate)Neighbor(Cate, David) Blond(Ann) ¬ Blond(David) where Neighbor is a symmetric and transitive role.  Does A have a model?  Is Bob an instance of the following concepts in all models of A ? ∃ Likes.(Blond ⊓∃ Neighbor. ¬ Blond) ∃ Likes.( ∃ Neighbor.( ∀ Neighbor. ¬ Blond)) 13

14 Exercise on Tableaux  The tableaux is an algorithm to check satisfiability.  If all branches of your tableaux are open, then? You cannot say it is valid! Why? OWA!  If all branches of your tableaux are closed, then? You can say it is unsatisfiable  What can we do with tableaux? To prove the satisfiability of a concept. 14

15 Tableaux cont.  Rules  ⊓  ⊔  ∃  ∀ …  Exercises 1. Are these subsumptions valid? ∀ R.A ⊓∀ R.B ⊑∀ R.(A ⊓ B) ∃ R.A ⊓∃ R.B ⊑∃ R.(A ⊓ B) 2. Decide whether the following subsumption holds ¬∀ R.A ⊓∀ R.C ⊑ T ∀ R.D with T ={C ≡ ( ∃ R.B) ⊓¬ A, D ≡¬(∃ R.A) ⊓∃ R.( ∃ R.B)} 15

16 RelBAC: Domain Specific DLs  Syntax  Nc: subject groups, object types;  Ni: individual subjects, individual objects;  Nr: permissions  DL constructors and formation rules  Semantics  Hierarchy  Permission assignment  Ground assignment  Chinese Wall  SoD  High Level SoD  Queries 16 Policies Properties

17 RelBAC Modeling  “The LDKR course consists of:  For persons: Prof. Giunchiglia and TA Zhang as lecturers, Student Tin, Hoa, Parorali, Sartori, Chen, Gao, Lu, Zhang;  For online materials: syllabus, slides for lectures, references, exercises and keys, exam questions, results and marks.”  We know that,  Slides can be updated only by professors or TA;  Students can download all materials but only update keys to exercises.  Each student should upload exam result to the site that TA can read and check for propose marks which will be finally decided by professor(s). 17

18 LDKR Modeling Answer LDKR Person LecturerProfessorTAStudentPhDMaster 18 MaterialSyllabusLectureSlideExerciseSlideKeyExamQuestionKey Update Download Upload

19 Chinese Wall Property  Chinese Wall (CW) “Originally no one has any access to anything; then some requests are accepted and someone is allowed to perform some operation on something; from then on, those has been allowed to access should not be allowed to access on those things arousing conflict of interests.”  Conflict of Interests (COI) Resources in COI should be avoided access for disclosure of information about competing parties. 19 ObjectBankBTBUniCreditRurarePopolareCompanyLenovoApple COI

20 Modeling of the Chinese Wall Property  Given a COI = {A1, …, An}, if one can access Ai, then s/he should not be allowed to access the rest.  Suppose for Ai, the permission is Pi, then ⊔ 1 ≤ i<j ≤ n ∃ Pi.Ai ⊓∃ Pj.Aj ⊑⊥ 20

21 SoD  Separation of Duties…  Intuition  Definition  Semantic Details  MEP  MEO  FA  IFA 21

22 Mutually Exclusive Positions  A ‘position’ is an organizational role denoting a group of subjects such as employees, managers, CEOs, etc. Given a set of positions P = {P1, …, Pn}, where each Pi is a concept name:  To enforce that a subject can be assigned to at most one position among P.  To enforce that no subject can be assigned to all the positions in P.  To enforce that a subject can be assigned to at most m positions among P. 22

23 Exercise of MEP  In a bank scenario, customers sign checks; bank clerks cash out the checks and managers monitor the checks.  MEP: ‘one can play at most one of the positions as customer, clerk and manager.’ 23

24 Exercise of MEP cont.  MEP: ‘no one can play more all of the positions as customer, clerk and manager.’  MEP: ‘one can play at most 1 of the positions as customer, clerk and manager.’ 24

25 Mutually Exclusive Operations  An `operation’ is a kind of permission that subjects may be allowed to perform some `act’ on objects, such as Read, Download, etc. Given a set of operations giving rise to a MEO, OP = {Op1, …, Opn} (where each Opi is a DL role name), then, we distinguish two different kinds of MEO:  To enforce that a subject cannot perform any two operations in OP.  To enforce that a subject cannot perform any two operations in OP on the same object. 25

26 Exercise of MEO  Suppose a common file repository scenario: files are objects, users are clients that visit the repository and permissions are read or write.  MEO: ‘one cannot read and write at the same time.’  MEO: ‘one cannot read and write at the same time on the same file.’ Notice the difference between the two MEO’s. 26

27 Functional Access and Inverse  FA: A permission is functional iff it connects at most one object in the range.  If each user in U, has an FA, P, to an object in O, then  IFA: A permission is inverse functional iff it connects at most one subject in the domain.  If each object in O, has and IFA, P -, from a user in U, then 27

28 Exercise of FA and IFA  Give a scenario where FA and IFA are necessary.  Desktop usage in lab.  Bank private manager/clerk service … 28

29 OWL  OWL Lite: originally intended to support those users primarily needing a classification hierarchy and simple constraints.  OWL DL: to provide the maximum expressiveness possible while retaining computational completeness, decidability and the availability of practical reasoning algorithms.  OWL Full: designed to preserve some compatibility with RDF Schema. 29 SyntaxSemanticsExpressivenessComputability OWL LitesimpleDL subsetHierarchy, simple constraintsEfficient OWL DL SHIO(D) DLMaximum possible expressivenessExist OWLRDF Compatible with RDFNon

30 Exercise of OWL  Refer to document specification… 30


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