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Semantic Web Technologies

Semantic Web Technologies. The Semantic Web: Means Many Things to Many People. What is the Semantic Web?. The Semantic Web is an evolving extension of the World Wide Web in which web content can be expressed not only in natural language,

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Semantic Web Technologies

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  1. Semantic Web Technologies

  2. The Semantic Web:Means Many Things to Many People

  3. What is the Semantic Web? The Semantic Web is an evolving extension of the World Wide Web in which web content can be expressed not only in natural language, but also in a form that can be understood, interpreted and used by software agents, thus permitting them to find, share and integrate information more easily. It derives from W3C director Tim Berners-Lee's vision of the Web as a universal medium for data, information, and knowledge exchange.

  4. The Story

  5. The Story Analyzed • Agent finds hospitals based on attributes: • Compatible insurance plan • Compatible location • Available appointment times • Available services • Required quality rating • Requires: • Access to hospital data • Access to personal data (query, calendar, location) • Data includes all attribute values • Compare hospitals’ and query’s attribute values (unified rep) • Explain decisions and inferences

  6. Challenges • Missing data • Extra data • Different representations of data (vocabulary): • Different units of measure • Different names for same entities, attributes, values • Same names for different entities, attributes, values • Different granularity of entity classes

  7. Solutions • Missing data  Infer from available data. • Extra data  Filter using relevance. • Different representations of data  Unify representations by mapping between sources. • Summary: Inference based on unified (general) knowledge representation.

  8. Inference • What is inference? • Process of deriving new facts from old facts. “Reasoning”. • What is a proof? • A demonstration that a deductive inference is sound (= true, assuming logic is correct). • What is logic? • The rules used to perform deductive inference.

  9. The Layer Cakeof the Semantic Web

  10. OSM/OWL & Logic and Proof • Model the application domain ontologically (high level of abstraction). • Systematically transform application model into predicate calculus. • Perform reasoning on predicates.

  11. Ontology Language: OSM & OWL <owl:Ontology> … <owl:Class rdf:ID=“Italian”> <owl:unionOf rdf:parseType=“owl:collection”> <owl:Class ref:resource=“#Lazy”/> <owl:Class ref:resource=“#Mafioso”/> <owl:Class ref:resource=“#LatinLover”/> … <owl:Restriction> <owl:onProperty rdf:resource=“#hasName”/> <owl:minCardinality> <rdf:datatype=“&xsd:#nonNegativeInteger”>1</…> </owl:minCardinality> <owl:maxCardinality> … <owl:Class rdf:ID=“LatinLover”> <rdfs:subClassOf rdf:resource=“#Italian”/> <owl:disjointWith rdf:resource=“#Lazy”/> <owl:disjointWith rdf:resource=“#Mafioso”/> … <owl:ObjectProperty rdf:ID=“hasName”> <rdfs:domain rdf:resource=“#Italian”/> <rdfs:range rdf:resource=“#Name”/> ...

  12. Transformation toPredicate Calculus: Example

  13. Generated Predicates 1. Object Sets Room(x), Room Nr(x), Cost(x), Date(x), Guest(x), Guest Nr(x), Current Guest(x), Future Guest(x), Guarantee Nr(x) 2. Relationship Sets Room(x) has Room Nr(y), Room(x) has Cost(y), Guest(x) has reservation for Room(y) on Date(z), Guest(x) has Guest Nr(y) Future Guest(x) has Guarantee Nr(y)

  14. 3. Generated Rules Referential-Integrity Constraints xy(Room(x) has Room Nr(y)  Room(x)  Room Nr(y)) ... Generalization/Specialization Constraints x(Current Guest(x)  Future Guest(x)  Guest(x)) Participation Constraints x(Room(x)  1y(Room(x) has Cost(y)) x(Cost(x) 1y(Room(y) has Cost(x)) ... Co-occurrence Constraints <x, y>(z(Guest(z) has reservation for Room(x) on Date(y)) 1w(Guest(w) has reservation for Room(x) on Date(y)))

  15. A Valid Interpretation Object-Set Relations Room R1 R2 Room Nr 1 2 Cost 90 80 ... Relationship-Set Relations Room has Room Nr R1 1 R2 2 ... Constraints x(Room(x) 1y(Room(x) has Room Nr(y)) ...

  16. Logic and Proof Prove: ItalianProf(x)  LatinLover(x) Proof (by contradiction): • LatinLover(x) negation of conclusion • ItalianProf(x) premise • ItalianProf(x)  Italian(x) ontologically given • Italian(x) modus ponens (2&3) • Italian(x)  Lazy(x)  Mafioso(x)  LatinLover(x) ontologically given • Lazy(x)  Mafioso(x)  LatinLover(x) modus ponens (4&5) • Lazy(x)  Mafioso(x) resolution (1&6) • ItalianProf(x)  Lazy(x) ontologically given • Lazy(x) modus ponens (2&8) • Mafioso(x) resolution (7&9) • Mafioso(x)  ItalianProf(x) ontologically given • ItalianProf(x) modus ponens (10&11) • F contradiction (2&12)

  17. OWL Foundation: RDF (Resource Description Framework) • A triple model: • Every assertion is decomposed in three parts • (subject, predicate, object) • For instance (tutorial.php, author, "Fabien") • The subject is a URI identifying a resource. • The predicate is a binary relation identified by a URI. • The object is either a URI identifying a resource or a literal value. • Each triple illustrated as a labeled arc. • A set of statements/arcs is a graph of relations and attributes of URI resources.

  18. RDFS (RDF Schema) • A set of primitives to describe lightweight ontologies, allowing us to: • Name the resource types and binary relation (property) types. • Specify signature of properties: • Type of domain = type of subject • Type of range = type of object • Specify inheritance between classes (subClassOf); • Specify inheritance between properties (subPropertyOf); • Includes multiple inheritance.

  19. RDF Graph

  20. RDF vs. RDBMS/SQL • Similarities: • Based on relational models • Knowledge representations • Integrity constraint / inference engines • Differences: • Different relational models (e.g. only binary relations in RDF) • In RDBMS, a missing tuple means that statement is false (closed-world assumption) • In RDF, a missing tuple means unknown (open-world assumption) • Links: • http://www.rdfabout.com/comparisons.xpd

  21. N-ary Relation in RDF • John buys a "Lenny the Lion" book from books.example.com for $15 as a birthday gift. • There is a relation, in which individual John, entity books.example.com and the book Lenny_the_Lion participate. • This relation has other components as well such as the purpose (birthday_gift) and the amount ($15).

  22. N-ary Relation in RDF (Reification) http://www.w3.org/TR/swbp-n-aryRelations/#example3

  23. Querying RDF Data • RDF data is a graph. • Resources/entities and attributes values are nodes. • Relationships and properties are edges. • Queries specify constraints on sub-graphs. • Executing queries returns matching sub-graphs.

  24. SPARQL (An RDF Query Language) • Specifies queries over an RDF triple store • Triple stores have an OWL/RDF schema • Example: get names and, if available, gender and birthdate of people born in the 1870’s: SELECT ?Name ?Gender ?BirthDate WHERE { ?IndividualURI gc:name ?Name . OPTIONAL { ?IndividualURI gc:gender ?Gender ; gc:born ?Birth . ?Birth gc:date ?BirthDate } . FILTER REGEX(?BirthDate, "187\\d") }

  25. Genesis

  26. Querying RDF Data • Links: • http://www.youtube.com/watch?v=3kdDKILwna0 • http://www.dailymotion.com/video/xewx1o_scribo-workbench-sparql-query-edito_tech • http://www.youtube.com/watch?v=8F-gT02ZyDs

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