Semantic Web Layers. How Semantic Languages Work. Why Go Beyond XML? Bring information together Draw inferences for further processing RDF OWL RDFS

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1 Semantic Web Layers Module XIII: Extending OWL with Rules Executive Overview of Semantic languages Copyright 2011 slide 1 Why Go Beyond XML? Issue Same word has two meanings Suppliers and Consumers agree on what tags to use (maybe) Suppliers and Consumers agree on what tags mean (less likely) Each item is described by only one supplier (sure!) Describe what tags to use, how to use them (syntax) Manage distributed definitions and content A small amount of goes a long way Jim Hendler Express relationships between elements subclass, domain, range Express semantics for concepts, instances and relationships sameas, differentfrom, equivalentclass, intersectionof, unionof, complementof, disjointwith cardinality, transitive, inverseof, symmetricproperty, restriction, subpropertyof, FunctionalProperty, InverseFunctionalProperty allvaluesfrom, somevaluesfrom, hasvalue, oneof Manage evolution of schema and content DeprecatedClass, DeprecatedProperty, versioninfo, priorversion, backwardcompatiblewith, incompatiblewith Solution XML Namespaces XML Schema S OWL OWL How Semantic Languages Work Bring information together Draw inferences for further processing S OWL Copyright TopQuadrant Inc., TopMIND Training -Getting Ready for the Semantic Web, slide 2 Copyright TopQuadrant Inc., TopMIND Training -Getting Ready for the Semantic Web, slide 3 What is? A Distributed Network of Data! (Resource Description Framework) is an infrastructure for: Encoding, Exchange and Distributing metadata Triple: Files: bags of triples Facial offeredby Subject Predicate Object offeredby Facial Copyright TopQuadrant Inc., TopMIND Training -Getting Ready for the Semantic Web, slide 4 Copyright TopQuadrant Inc., TopMIND Training -Getting Ready for the Semantic Web, slide 5 1

2 S A Schema Language for S is, Too! S allows us to create vocabularies Resort rdfs:subclassof Spa Activity rdfs:subclassof Treatment SafetyHarbor Spa Resort rdfs:subclassof If the bags contain S key symbols, then S can infer certain conclusions Spa rdf:type rdf:type rdf:type Resort rdfs:subclassof Spa Activity rdfs:subclassof Treatment rdfs:range rdf:type Copyright TopQuadrant Inc., TopMIND Training -Getting Ready for the Semantic Web, slide 6 Copyright TopQuadrant Inc., TopMIND Training -Getting Ready for the Semantic Web, slide 7 OWL Specify Rich Relationships The Kennedy Family MalePerson SafetyHarbor offeredby Facial Joseph Rose Person hasson rdfs:range offeredby hasdaughter Person rdfs:range FemalePerson Joseph Jr. John Robert Edward owl:inverseof offeredby Has-child subpropertyof Rosemary Kathleen Eunice Patricia Jean hasdaughter hasson Copyright TopQuadrant Inc., TopMIND Training -Getting Ready for the Semantic Web, slide 8 Copyright 2011 slide 9 OWL vs. vs. S OWL vs. Other Schema (Data Model) Languages OWL and S add no syntax to allows symbols from any package S uses symbols from the S package OWL uses symbols from the OWL package Database schema is used to interpret schema is used for inference Syntactically, = S = OWL OWL Model Can have multiple uses The difference is in the inference XML schema is used to Validate Object Models define behavior Copyright 2011 slide 10 Copyright 2011 slide 11 Copyright 2011 slide 11 2

3 Limitations of, S & OWL Rules Complement /S/OWL Class definition and reasoning requiring numerical computation For example, VIP customers are those that spent over $1,000 across all purchases in the last 3 months For example, a young man is a MalePerson who is less than 20 years old. Classification of an individual depends on examining specific relationships between 2 or more other individuals: For example, city s first lady is a wife of city s mayor. Mr. Bloomberg is New York s mayor. Mrs. Bloomberg is his wife. Is Mrs. Bloomberg New York s first lady? Copyright 2011 slide 12 Copyright 2011 slide 13 3

4 Module XII Extending OWL with Rules Allegro Prolog Limitations of, S & OWL Class definition and reasoning requiring numerical computation For example, VIP customers are those that spent over $1,000 across all purchases in the last 3 months For example, a young man is a MalePerson who is less than 20 years old. Classification of an individual depends on examining specific relationships between 2 or more other individuals: For example, city s first lady is a wife of city s mayor. Mr. Bloomberg is New York s mayor. Mrs. Bloomberg is his wife. Is Mrs. Bloomberg New York s first lady? Copyright 2011 slide 1 Copyright 2011 slide 2 Rules Complement /S/OWL Rules Technology Venerable tradition Expert Systems (1970 s and 1980 s) Rules embedded in application code Not scalable, extendable, maintainable, Business Rules Datalog (DATAbase LOGic) Sophisticated algorithms (RETE, Magic Sets) SWRL a rule exchange language in Copyright 2011 slide 3 Copyright 2011 slide 4 Rules and SWRL Rules can be distributed just like anything else SWRL Rules for Semantic Technology IF a person has been CEO of a big, Fortune 500 company THEN this person is an important person Therefore, rules can be expressed in SWRL (Semantic Web Rule Language) is a representation of rules in Rule name Body Major: (?company rdf:type BigCompany) (Fortune500 lists?company) (?company hasceo?person) -> (?person rdf:type ImportantPerson) Head Copyright 2011 slide 5 Copyright 2011 slide 6 1

5 OWL vs. SWRL SPARQL vs. SWRL OWL W3C Recommendation Recent implementations Formal decidability Restriction language highly constrained SWRL Not yet a standard >20 years technology Possibility of Spaghetti code Powerful pattern language SPARQL Complex patterns with?variables Defaults, options, boolean operations Filters with math Run under user/program control Optimized for a single query SWRL Complex patterns with?variables AND only SWRL built-ins for math chaining opportunistically Optimized for groups of rules Copyright 2011 slide 7 Copyright 2011 slide 8 Requirements for Rule Languages OWL vs. Rule Requirements Well-defined language semantics Formal decidability Expressive syntax Standard based Small set of composable logical operators Flexibility, reusability Extensible syntax Scalablility Apply rules to very large data set (e.g., > 1 billion triples) Requirement Well-defined language semantics Small set of composable logical operators Extensible syntax Scalablility OWL score Yes Yes No No The last two no s deserve more discussion... Copyright 2011 slide 9 Copyright 2011 slide 10 SPARQL vs. Rule Requirements CL / Prolog vs. Rule Requirements Requirement SPARQL has Requirement CL (Prolog) score Well-defined language semantics Procedural semantics for access language Well-defined language semantics Yes Small set of composable logical operators Kitchen sink Small set of composable logical operators Yes Extensible syntax Unfortunate choice of syntax Extensible syntax Yes Scalablility Yes, SPARQL scales Scalablility For subsets Copyright 2011 slide 11 Copyright 2011 slide 12 2

6 Semantic Database Needs Does SPARQL Qualify? A Simple QUERY language An efficient and easy to understand mechanism for RULES A server based PROGRAMMING LANGUAGE SPARQL is an access language, no rules, no programming. No defined way to deal with rules 2+ arity is hell, needs magic predicates to help Negation is hard Filters should go, replaced by magic predicates Datatype handling is bizarre No way to deal with unpredictable path lengths (yes, they are working on this) Prolog Almost Qualify Query language + Rules + Full programming language + a Standard language And very fast: AllegroGraph S++ reasoner is implemented in Prolog For 1.1 Billion triples for the Leheigh University Benchmark: LUBM(8000) with long queries zeroed AllegroGraph Rule Language Seconds Queries AllegroGraph 3.2 Other PROLOG Copyright 2011 slide 16 Prolog Rule Based On Unification Prolog Functor: append A function call returns an output value based on the input supplied (+ 1 (* 2 3) 4) => 11 (append (1 3) (5)) => (1 3 5) (member 3 '(1 3 5)) => (3 5) +, append and member are functions A Prolog functor will try to unify the input and output arguments (?- (append (1 3) (5)?z)) (?- (member?x (1 3 5))) (?- (append (1 3) (5)?z))?z = (1 3 5); (?- (append (1)?y (?x 3 5)))?y = (3 5)?x = 1; (?- (append?x?y (1 3 5)))?x = ()?y = (1 3 5);?x = (1)?y = (3 5);?x = (1 3)?y = (5);?x = (1 3 5)?y = (); Copyright 2011 slide 17 Copyright 2011 slide 18 3

7 Prolog Functor: member Allegro Prolog as Query Language (?- (member?x (1 3 5)))?x = 1;?x = 3;?x = 5; (?- (member 1 (?x 3 5)))?x = 1; (?- (member (1?x) ((?y 3) 5)))?x = 3?y = 1; Unify over database with q functor (q?person rdf:type :Patriarch) A much more powerful query language for triples, more versatile than SPARQL An industrial strength Prolog embedded in Allegro CL, geared to query Conform to Clocksin & Mellishs Prolog and ISO kernel specification Prolog clauses are compiled to Lisp then to machine code, running at processor speed Copyright 2011 slide 19 Copyright 2011 slide 20 Basic Allegro Prolog Syntax for Allegro Prolog Query Language The functor q- & q tells Prolog to look for data for variable unification in the AllegroGraph triple database q- only search the original triples q include inferred triples in the search By convention, all Prolog variables start with a?, e.g.,?company,?person, to separate them from Lisp variables A very expressive query language, finding semantic relations in database automatically Query can be applied to inferred triples, combining with semantic reasoning Semantic Applications Prolog Query Engine Encapsulated / Federated Database Asserted Triples S++ Reasoning Inferred Triples Copyright 2011 slide 21 Copyright 2011 slide 22 select Wrapper in Lisp (select ) returns the values of unified variables in a list, for further processing by application code (select (?x?y) (append?x?y (1 3 5))) (("nil" (1 3 5)) ((1) (3 5)) ((1 3) (5)) ((1 3 5) "nil")) (select (?person) (q-?person rdf:type :Patriarch)) (({person1})) Allegro Prolog as Rule Language Assert a fact or rule <-- clause : (<-- (ImportantPerson?person) ) starts a new rule <- clause : (<- (ImportantPerson?person) ) add to the rule Iteratively prove the concatenation of clauses (select?person (ImportantPerson?person) ) Allegro Prolog will look for data (maybe in database) that unifies the variables and satisfies ALL the clauses Copyright 2011 slide 23 Copyright 2011 slide 24 4

8 Rule in SWRL and Allegro Prolog Rule in SWRL Major: (?company rdf:type BigCompany) (Fortune500 lists?company) (?company hasceo?person) -> (?person rdf:type ImportantPerson) Rule in Prolog (2) Rule in Prolog (1) (<-- (MajorCompany?company) (<-- (ImportantPerson?person) (q?company (q?company rdf:type rdf:type BigCompany) BigCompany) (q Fortune500 (q Fortune500 lists lists?company)?company) ) (q (<--?company (ImportantPerson hasceo?person)?person) ) (MajorCompany?company) (q?company hasceo?person) ) Person The Kennedy Family hasdaughter rdfs:range Joseph FemalePerson Rose Rosemary Kathleen Eunice Patricia Jean Person hasson Joseph Jr. John Robert Edward hasdaughter Has-child subpropertyof MalePerson rdfs:range hasson Copyright 2011 slide 25 Copyright 2011 slide 26 Building Semantic Relations with Prolog on Database Building Complex Semantic Relations (<-- (male?x) (q-?x!ex:sex!ex:male)) (<-- (female?x) (q-?x!ex:sex!ex:female)) (<-- (father?x?y) (male?x) (q?x!ex:has-child?y)) (<-- (mother?x?y) (female?x) (q?x!ex:has-child?y)) (<-- (parent?x?y) (father?x?y)) (<-- (grandparent?x?y) (parent?x?z) (parent?z?y)) (<-- (grandchild?x?y) (grandparent?y?x)) (<-- (ancestor?x?y) (parent?x?y)) (<- (ancestor?x?y) (parent?x?z) (ancestor?z?y)) (<-- (descendent?x?y) (ancestor?y?x)) (<-- (aunt?x?y) (parent?z?x) (female?x) (parent?z?w) (not (=?x?w)) (parent?w?y)) (<-- (uncle?x?y) (parent?z?x) (male?x) (parent?z?w) (not (=?x?w)) (parent?w?y)) (<-- (nephew?x?y) (aunt?y?x) (male?x)) parent aunt?z (<-- (niece?x?y) (aunt?y?x) (female?x)) parent?x?w?y parent (<- (parent?x?y) (mother?x?y)) (<- (nephew?x?y) (uncle?y?x) (male?x)) (<- (niece?x?y) (uncle?y?x) (female?x)) Copyright 2011 slide 27 Copyright 2011 slide 28 Semantic Rule Inference Allegro Prolog rules can be applied to inferred triples, combining rule inference and semantic reasoning Prolog Rules Encapsulated / Federated Database Asserted Triples Semantic Applications Prolog Rule Engine S++ Reasoning Inferred Triples Interact with AllegroGraph Database All data values stored in the DB as UPI s; convert to its original value with lisp function upi->value, e.g., (upi->value?birth-year) Data need to be converted to UPI before it can be compared with db values, e.g., (literal Maria Shriver ) Use (read-from-string ) to convert a string (come out of DB) to its native type Copyright 2011 slide 29 Copyright 2011 slide 30 5

9 Execute Lisp Code inside Prolog Execute Prolog Rules Inside Lisp (lisp arg form) : Execute Lisp form (code) inside Prolog, the result of executing the form is returned to Prolog variable, arg (lisp form) : For side effect only (lispp form): unified if form returns true Copyright 2011 slide 31 (select (?name?birth-year) (q-?person rdfs:label?name) (q-?person :birth-year?birth-year) (lispp (>= (read-from-string (upi->value?birth-year)) 1965) ) ) (prolog ) : invoke prolog inside Lisp code (lisp arg form) : Execute Lisp code inside Prolog, the result is returned to arg (lisp form) : For side effect only Copyright 2011 slide 32 (defun animal-friends-of (person) (let ((list-friends nil)) (prolog (lisp?person person) (q?person :likes?x) (q?x rdf:type :animal) (lisp (pushnew?x list-friends))) list-friends)) Prolog Exercise Using Allegro Prolog in AllegroGraph Using Prolog query and rules on Kennedy database Use Prolog to Find Maria Shriver & Her Birth Year (select (?p?year) (lisp?name (literal "Maria Shriver ")) (q-?p!rdfs:label?name) (q-?p!ex:birth-year?year)) Assuming that the data has NOT been normalize Use lisp functor to bind a value from Lisp to Prolog Copyright 2011 slide 33 Copyright 2011 slide 34 Find All Ancestors of Maria Shriver Find All Ancestors of Maria Shriver (select (?name) (lisp?y!ex:person26) (ancestor?x?y) (q-?x!rdfs:label?name)) (select (?name) (:distinct t) (lisp?y!ex:person26) (ancestor?x?y) (q-?x!rdfs:label?name)) (let ((anc nil) (anc-list nil)) (setq anc (select (?name) (lisp?y!ex:person26) (ancestor?x?y) (q-?x!rdfs:label?name))) (dolist (e anc) (pushnew (first e) anc-list)) anc-list) Copyright 2011 slide 35 Copyright 2011 slide 36 6

10 Find Nieces of Edward Kennedy (select (?niece?name) (lisp?edward (literal "Edward Kennedy ")) (q-?p!rdfs:label?edward) (niece?niece?p) (q-?niece!rdfs:label?name)) Find Old Men (borne before 1950) in Kennedy Family (<-- (old-kennedy?x) (male?x) (q-?x!ex:birth-year?year) (lispp (< (read-from-string (upi->value?year)) 1950))) (select (?name) (old-kennedy?x) (q-?x!rdfs:label?name)) Copyright 2011 slide 37 Copyright 2011 slide 38 Old Living Men in Kennedy Family (<-- (OldMan?person) (q-?person!ex:birth-year?birth-year) (q?person!rdf:type!ex:maleperson) (not (q?person!ex:death-year?death-year)) <- Negation (lispp (< (read-from-string (upi->value?birth-year)) 1950)) ) (select (?name) (OldMan?x) (q-?x!rdfs:label?name) ) Young Men (borne after 1965) in Kennedy Family (<-- (YoungMan?person) (q-?person!ex:birth-year?birth-year) (q?person!rdf:type!ex:maleperson) (lispp (>= (read-from-string (upi->value?birth-year)) 1965)) ) (select (?name) (YoungMan?x) (q-?x!rdfs:label?name) ) Copyright 2011 slide 39 Copyright 2011 slide 40 Expressive Logics are Coming to Franz Rule Language No Semantics OWL First Order Logic Common Logic, Prolog RIF Logic Programs SILK Rule language for AllegroGraph store Has expressive syntax CLIF (Common Logic Interchange Format) Has a well-defined, constructive semantics Aligns as much as possible with an existing or emerging standard SPARQL Copyright 2011 slide 41 7

11 CLIF Translator Amdocs Intelligent Decision Automation Events Decision Engine Actions Prefix CLIF SPARQL Amdocs Event Collector Event Ingestion Container Container Inference Engine Amdocs Integration Framework Infix CLIF Translator ACL Prolog RM Events CRM NW Web 2.0 OMS Scheduled Events Business Rules Sesame Bayesian Belief Network CRM Work Bench Operational Systems Event Data Sources AllegroGraph Trillion Triples Triple Store DB Copyright slide 44 Thank You Dr. Sheng-Chuan Wu Copyright 45 8