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## Logic and Rules

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**Logic and Rules**Most material was taken from the following source: Grigoris Antoniou, Frank van Harmelen, A Semantic Web Primer, 2nd Edition, MIT Press, 2008, ISBN 978-0-262-01242-3.**Lecture Outline**• Introduction • Rules: Example • Rules: Syntax & Semantics • RuleML & RIF: XML Representation for Rules • Rules & Ontologies: OWL 2 RL & SWRL Logic and Rules**Knowledge Representation**• The subjects presented so far were related to the representation of knowledge • Knowledge Representation was studied long before the emergence of WWW in AI • Logic is still the foundation of KR, particularly in the form of predicate logic (first-order logic) Logic and Rules**The Importance of Logic**• High-level language for expressing knowledge • High expressive power • Well-understood formal semantics • Precise notion of logical consequence • Proof systems that can automatically derive statements syntactically from a set of premises Logic and Rules**The Importance of Logic (2)**• There exist proof systems for which semantic logical consequence coincides with syntactic derivation within the proof system • Soundness & completeness Logic and Rules**The Importance of Logic (3)**• Predicate logic is unique in the sense that sound and complete proof systems do exist. • Not for more expressive logics (higher-order logics) • Logic can trace the proof that leads to a logical consequence. • Logic can provide explanations for answers • By tracing a proof Logic and Rules**Specializations of Predicate Logic:RDF and OWL**• RDF/S and OWL (Lite and DL) are specializations of predicate logic • correspond roughly to a description logic • They define reasonable subsets of logic • Trade-off between the expressive power and the computational complexity: • The more expressive the language, the less efficient the corresponding proof systems Logic and Rules**Specializations of Predicate Logic:Horn Logic**• A rule has the form: A1, . . ., An B • Ai and B are atomic formulas • There are 2 ways of reading such a rule: • Deductive rules: If A1,..., An are known to be true, then B is also true • Reactive rules: If the conditions A1,..., An are true, then carry out the action B Logic and Rules**Description Logics vs. Horn Logic**• Neither of them is a subset of the other • It is impossible to assert that persons who study and live in the same city are “home students” in OWL • This can be done easily using rules: studies(X,Y), lives(X,Z), loc(Y,U), loc(Z,U) homeStudent(X) • Rules cannot assert the information that a person is either a man or a woman • This information is easily expressed in OWL using disjoint union Logic and Rules**Exchange of Rules**• Exchange of rules across different applications • E.g., an online store advertises its pricing, refund, and privacy policies, expressed using rules • The Semantic Web approach is to express the knowledge in a machine-accessible way using one of the Web languages we have already discussed • We show how rules can be expressed in XML-like languages (“rule markup languages”) Logic and Rules**Lecture Outline**• Introduction • Rules: Example • Rules: Syntax & Semantics • RuleML & RIF: XML Representation for Rules • Rules & Ontologies: OWL 2 RL & SWRL Logic and Rules**Family Relations**• Facts in a database about relations: • mother(X,Y), X is the mother of Y • father(X,Y), X is the father of Y • male(X), X is male • female(X), X is female • Inferred relation parent: A parent is either a father or a mother mother(X,Y) parent(X,Y) father(X,Y) parent(X,Y) Logic and Rules**Inferred Relations**• male(X), parent(P,X), parent(P,Y), notSame(X,Y) brother(X,Y) • female(X), parent(P,X), parent(P,Y), notSame(X,Y) sister(X,Y) • brother(X,P), parent(P,Y) uncle(X,Y) • mother(X,P), parent(P,Y) grandmother(X,Y) • parent(X,Y) ancestor(X,Y) • ancestor(X,P), parent(P,Y) ancestor(X,Y) Logic and Rules**A More Complex Rule ExampleBrokered Trade**• Brokered trades take place via an independent third party, the broker • The broker matches the buyer’s requirements and the sellers’ capabilities, and proposes a transaction when both parties can be satisfied by the trade • The application is apartment renting an activity that is common and often tedious and time-consuming Logic and Rules**The Potential Buyer’s Requirements**• At least 45 m2 with at least 2 bedrooms • Elevator if on 3rd floor or higher • Pet animals must be allowed • Carlos is willing to pay: • 300 € for a centrally located 45 m2 apartment • 250 € for a similar flat in the suburbs • An extra 5 € per m2 for a larger apartment • An extra 2 € per m2 for a garden • He is unable to pay more than 400 € in total Logic and Rules**The Potential Buyer’s Requirements (2)**• If given the choice, he would go for the cheapest option • His second priority is the presence of a garden • His lowest priority is additional space Logic and Rules**Formalization of Carlos’s Requirements – Predicates Used**• apartment(x), x is an apartment • size(x,y), y is the size of apartment x (in m2) • bedrooms(x,y), x has y bedrooms • price(x,y), y is the price for x • floor(x,y), x is on the y-th floor • gardenSize(x,y), x has a garden of size y Logic and Rules**Formalization of Carlos’s Requirements – Predicates Used**(2) • lift(x), there is an elevator in the house of x • pets(x), pets are allowed in x • central(x), x is centrally located • acceptable(x), flat x satisfies Carlos’s requirements • offer(x,y), Carlos is willing to pay y € for flat x Logic and Rules**Formalization of Carlos’s Requirements – Rules**acceptable(X):- apartment(X), not(incompatible(X)). incompatible(X):- bedrooms(X,Y), Y < 2. incompatible(X):- size(X,Y), Y < 45. incompatible(X):- not(pets(X)). incompatible(X):- floor(X,Y), Y > 2, not(lift(X)). incompatible(X):- price(X,Y), Y > 400 . Logic and Rules**Formalization of Carlos’s Requirements – Rules (2)**offer(X,O) :- size(X,Y), Y ≥ 45, garden(X,Z), central(X), O = 300 + 2*Z+5*(Y-45). offer(X,O) :- size(X,Y), Y ≥ 45, garden(X,Z), not(central(X)), O = 250+ 2*Z+5*(Y-45). incompatible(X):- offer(X,Y), price(X,Z), Y < Z. Logic and Rules**Selecting an ApartmentAuxiliary predicates**cheapest(X) :- acceptable(X), price(X,P1), not( (acceptable(Y), price(Y,P2), P2 < P1) ). largestGarden(X):- acceptable(X), gardenSize(X,G1), not( (acceptable(Y), gardenSize(Y,G2), G2 > G1) ). largest(X):- acceptable(X), size(X,S1), not( (acceptable(Y), size(Y,S2), S1<S2) ). Logic and Rules**Selecting an Apartment**cll(X):- cheapest(X), largestGarden(X), largest(X). cl(X) :- cheapest(X), largestGarden(X). c(X) :- cheapest(X). rent(X):- cll(X). rent(X):- cl(X), not(cll(Y)). rent(X):- c(X), not(cl(Y)). Logic and Rules**Representation of Available Apartments**pets(a1) garden(a1,0) price(a1,300) apartment(a1) bedrooms(a1,1) size(a1,50) central(a1) floor(a1,1) Logic and Rules**Representation of Available Apartments (2)**Logic and Rules**Lecture Outline**• Introduction • Rules: Example • Rules: Syntax & Semantics • RuleML & RIF: XML Representation for Rules Logic and Rules**Rules – Syntax**loyalCustomer(X), age(X) > 60 discount(X) • We distinguish some ingredients of rules: • variables which are placeholders for values: X • constants denote fixed values: 60 • Predicatesrelate objects: loyalCustomer, > • Function symbols which return a value for certain arguments: age Logic and Rules**Rules**B1, . . . , Bn A • A, B1, ... , Bn are atomic formulas • A is the head of the rule • B1, ... , Bn are the premises (body of the rule) • The commas in the rule body are read conjunctively • Variables may occur in A, B1, ... , Bn • loyalCustomer(X), age(X) > 60 discount(X) • Implicitly universally quantified Logic and Rules**Facts and Logic Programs**• A fact is an atomic formula • E.g. loyalCustomer(a345678) • The variables of a fact are implicitly universally quantified. • A logic program P is a finite set of facts and rules. Logic and Rules**Goals**• A goal denotes a query G asked to a logic program • The form: • B1, . . . , Bn OR • ?- B1, . . . , Bn. (as in Prolog) Logic and Rules**Proof Procedure**• We use a proof technique from mathematics called proof by contradiction: • Prove that A follows from B by assuming that A is false and deriving a contradiction, when combined with B • In logic programming we prove that a goal can be answered positively by negating the goal and proving that we get a contradiction using the logic program • E.g., given the following logic program we get a logical contradiction Logic and Rules**An Example**p(a) ¬X p(X) • The 2nd formula says that no element has the property p • The 1st formula says that the value of a does have the property p • Thus X p(X) follows from p(a) contradiction Logic and Rules**First-Order Interpretation of Goals**p(a) p(X) q(X) q(X) • q(a) follows from the logical program • X q(X) follows from the logical program • Thus, “logical program”{¬Xq(X)} is unsatisfiable, and we give a positive answer Logic and Rules**First-Order Interpretation of Goals (2)**p(a) p(X) q(X) q(b) • We must give a negative answer because q(b) does not follow fromthe logical program Logic and Rules**Carlo ExampleDetermining Acceptable Apartments**• If we match Carlos’s requirements and the available apartments, we see that • flat a1 is not acceptable because it has one bedroom only • flats a4 and a6 are unacceptable because pets are not allowed • for a2, Carlos is willing to pay $ 300, but the price is higher • flats a3, a5, and a7 are acceptable Logic and Rules**Inference for room a1**apartment(a1). bedrooms(a1,1). acceptable(X):- apartment(X), not(incompatible(X)). incompatible(X):- bedrooms(X,Y), Y < 2. ?- acceptable(a1). FALSE apartment(a1), not(incompatible(a1))FALSE bedrooms(a1,1), 1 < 2 TRUE Logic and Rules**Inference for room a3**apartment(a3). bedrooms(a3,2). size(a3,65). floor(a3,2). pets(a3). garden(a3,0). price(a3,350). acceptable(X):- apartment(X), not(incompatible(X)). ?- acceptable(a3). apartment(a3), not(incompatible(a3)) In order for «not» to becometrue, all alternative ways to proveincompatibility must beproven false Logic and Rules**Formalization of Carlos’s Requirements – Rules**incompatible(a3):- bedrooms(a3,2), 2 < 2.FALSE incompatible(a3):- size(a3,65), 65 < 45. FALSE incompatible(a3):- not(pets(a3)). FALSE incompatible(a3):- floor(a3,2), 2 > 2, not(lift(a3)). FALSE incompatible(a3):- price(a3,350), 350 > 400. FALSE incompatible(a3):- offer(a3,350), price(a3,350), 350 < 350. FALSE offer(a3,350) :- size(a3,65), 65 ≥ 45, garden(a3,0), not(central(a3)), 350 = 250+ 2*0+5*(65-45). Logic and Rules**Selecting the “best” apartment**• ?- rent(a3). • No • ?- rent(a5). • Yes • ?- rent(a7). • No • Among the acceptable (compatible) apartments a3, a5, a7, apartments a3 and a5 are cheapest. • Of these, a5 has the largest garden. • Thus a5 is suggested for renting. Logic and Rules**Lecture Outline**• Introduction • Rules: Example • Rules: Syntax & Semantics • RuleML & RIF: XML Representation for Rules • Rules & Ontologies: OWL 2 RL & SWRL Logic and Rules**Rule Markup Language (RuleML)**• RuleML: effort to develop markup of rules on the web • A familyof rule languages, corresponding to different kinds of rule languages: • derivation rules, integrity constraints, reaction rules, … • The kernel of the RuleML family is Datalog • function-free Horn logic • RuleML is experimental • studies various features of rule languages that are far from being standardized (e.g. nonmonotonic rules) • These efforts may feed into future standards • RuleML results were important in the development of RIF Logic and Rules**The discount for a customer buying a product is 7.5%**if the customer is premium and the product is luxury <Implies> <then> <Atom> <Rel>discount</Rel> <Var>customer</Var> <Var>product</Var> <Ind>7.5 percent</Ind> </Atom> </then> <if> <And> <Atom> <Rel>premium</Rel> <Var>customer</Var> </Atom> <Atom> <Rel>luxury</Rel> <Var>product</Var> </Atom> </And> </if> </Implies> Logic and Rules**Atomic Formulas**• p(X, a, f(b, Y)) <Atom> <Rel>p</Rel> <Var>X</Var> <Ind>a</Ind> <Expr> <Fun>f</Fun> <Ind>b</Ind> <Var>Y</Var> </Expr> </Atom> Logic and Rules**Facts**p(a). <Fact> <Atom> <Rel>p</Rel> <Ind>a</Ind> </Atom> </Fact> Logic and Rules**Rules**p(X,a), q(Y,b) r(X,Y) <If> <And> <Atom><Rel>p</Rel> <Var>X</Var> <Ind>a</Ind> </Atom> <Atom><Rel>q</Rel> <Var>Y</Var> <Ind>b</Ind> </Atom> </And> </If> </Implies> <Implies> <Then> <Atom> <Rel>r</Rel> <Var>X</Var> <Var>Y</Var> </Atom> </Then> Logic and Rules**RuleML: RuleMarkupLanguageDTD (one of the many variations)**<!ELEMENT rulebase((Implies|Fact)*)> <!ELEMENT Fact (Atom)> <!ELEMENT Implies ((If,Then)|(Then,If))> <!ELEMENT If (Atom)> <!ELEMENT Then (Atom|Naf|And|Or)> <!ELEMENT Atom (Rel,(Ind|Var|Expr)*)> <!ELEMENT Naf (Atom)+)> <!ELEMENT And (Atom)+)> <!ELEMENT Or (Atom)+)> <!ELEMENT Rel (#PCDATA)> <!ELEMENT Var (#PCDATA)> <!ELEMENT Ind (#PCDATA)> <!ELEMENT Expr (Fun, (Ind|Var|Expr)*)> <!ELEMENT Fun (#PCDATA)> Logic and Rules**Carlo – RuleML equivalent**incompatible(X):- not(pets(X)). Negation As Failure <Implies> <Then> <Atom> <Rel>incompatible</Rel> <Var>x</Var> </Atom> </Then> <If> <Naf> <Atom> <Rel>pets</Rel> <var>x</var> </Atom> </Naf> </If> </Implies> Logic and Rules**Rule Interchange Format: RIF**• Rule technology exhibits a broad variety • e.g. action rules, first order rules, logic programming • The aim of the W3C RIF Working Group • not to develop a new rule language that would fit all purposes, • focus on the interchange among the various Web rules • A family of languages, called dialects (2 kinds) • Logic-based dialects. Based on some form of logic • e.g. first-order logic, and various logic programming approaches • RIF Core, essentially corresponding to function-free Horn logic • RIF Basic Logic Dialect (BLD), Horn logic withequality. • Rules with actions (Production systems and reactive rules) • Production RuleDialect (RIF-PRD). Logic and Rules**RIF-BLD**• Corresponds to Horn logic with equality plus • Data types and built ins, and • Frames. • Data Types • integer, boolean, string, date, • “Built-in” Predicates • numeric-greater-than, startswith, date-less-than • Functions • numeric-subtract, replace, hours-fromtime Logic and Rules**An actor is a movie star if he has starred in more than 3**successful movies, produced in a span of at least 5 years. A film is considered successful if it has received critical acclaim (e.g. rating >8) or was financially successful (produced >$100M in ticket sales). These rules should be evaluated against the DBpedia data set. Logic and Rules**RIF – use of frames**• The use of frames has a long tradition in OO languages and knowledge representation, • Has also been prominent in rule languages (e.g. FLogic). • The basic idea is to represent objects as frames, and their properties as slots. • E.g., a class professor with slots name, office, phone, department etc. oid[slot1 -> value1 … slotn -> valuen] Logic and Rules