1 / 91

An Introduction to the Semantic Web Part 1: XML, RDF and RDFS Jyotishman Pathak , AI Lab, Iowa State University jpathak@

An Introduction to the Semantic Web Part 1: XML, RDF and RDFS Jyotishman Pathak , AI Lab, Iowa State University jpathak@cs.iastate.edu@cs.iastate.edu Outline Introduction & Motivation XML RDF & RDFS DL OWL Future Look & Resources World Wide Web WWW:

paul
Télécharger la présentation

An Introduction to the Semantic Web Part 1: XML, RDF and RDFS Jyotishman Pathak , AI Lab, Iowa State University jpathak@

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. An Introduction to the Semantic Web Part 1: XML, RDF and RDFS Jyotishman Pathak , AI Lab, Iowa State University jpathak@cs.iastate.edu@cs.iastate.edu ISU Artificial Intelligence Research Laboratory

  2. Outline • Introduction & Motivation • XML • RDF & RDFS • DL • OWL • Future Look & Resources ISU Artificial Intelligence Research Laboratory

  3. World Wide Web • WWW: • A global networkthat allows us to find, share, and combine information • A web of links • Information is represented using: • Natural Language (e.g., English) • Graphics, Multimedia.. • “O.K.” for humans to comprehend • Difficult for machine processing • Ambiguity, Unconstrained data formats.. ISU Artificial Intelligence Research Laboratory

  4. Example : Searching • Current search engines = keywords • Sensitive to syntax • Insensitive to semantics • High recall, low precision • Query: How many cows are there in Iowa? = 1,234,567 ISU Artificial Intelligence Research Laboratory

  5. Example: Data Integration • Databases are different in terms of structure, content • Applications require managing several databases • After company mergers (e.g., K-Mart & Sears) • Biochemical, Genetics etc.. • Semantics of the data(base) need to be specified explicitly (e.g., price & cost) ISU Artificial Intelligence Research Laboratory

  6. What is required ? • Ability for a resource to provide information about itself • Better known as “metadata” • E.g., Price – refers to price of an item without taxes • Ability to represent/store this information in machine-interpretable format • Ability to design vocabularies which would give well-defined meaning to the information • E.g., Pricemeans the same as Cost • Ability for agents to be able to reason about the (meta)data • E.g., if B brother of A, C brother of B => C brother of A • The solution : Semantic Web ISU Artificial Intelligence Research Laboratory

  7. The Semantic Web • A global network in which information is given well-defined meaning, better enabling computers and people to work in cooperation • Existing WWW is very human-oriented (E.g., Google) • A metadata based infrastructure for reasoning on the Web • Herbivorous animals eat grass, Cow is herbivorous.. • Extends the current web, doesn’t replace it..! ISU Artificial Intelligence Research Laboratory

  8. Semantic Web Layer Cake ISU Artificial Intelligence Research Laboratory

  9. Outline • Introduction & Motivation • XML • RDF & RDFS • DL • OWL • Future Look & Resources ISU Artificial Intelligence Research Laboratory

  10. Road Map We are here ISU Artificial Intelligence Research Laboratory

  11. XML • A text-based meta-language format for data exchange • Provides a pathway to transfer data easily between various applications • Markup or Tags – identifies structures in the document (<name> </name>) • XML Schema – provides a schema to XML files • XML Query – a typed query language for XML documents ISU Artificial Intelligence Research Laboratory

  12. An Example Michael Jackson has a homepage http://www.michaeljackson.com and is the artist of album Bad <body> <p>Michael Jackson has homepage <ahref="http://www.michaeljackson.com"> http://www.michaeljackson.com</a> and is the artist of album <ahref="http://www.music.org/songs/mj/Bad">Bad</a></p> </body> <artist><name>Michael Jackson</name><homepage>http://www.michaeljackson.com</homepage><album>Bad</album></artist> ISU Artificial Intelligence Research Laboratory

  13. XML file: a labeled tree <artist><name>…</name><homepage>…</homepage><album>…</album></artist> Structure or Syntax artist name album homepage ISU Artificial Intelligence Research Laboratory

  14. Can XML provide Semantics ? <Predator>……</Predator> • An un-manned aerial vehicle used by USAF for reconnaissance • An organism that lives by preying on other organisms • A company which specializes in manufacturing camouflage attire • A movie by the current Governor of California ISU Artificial Intelligence Research Laboratory

  15. Limitations of XML • Makes no commitment towards domain-specific vocabulary • Interoperability (of meaning) feasible only for closed collaboration • agents in a small & stable community • pages on a small & stable intranet • Not suitable for sharing information in WWW ISU Artificial Intelligence Research Laboratory

  16. Outline • Introduction & Motivation • XML • RDF & RDFS • DL • OWL • Future Look & Resources ISU Artificial Intelligence Research Laboratory

  17. Road Map We are here ISU Artificial Intelligence Research Laboratory

  18. What is Resource Description Framework ? • Defines a framework for structuring & describing resources (e.g., documents) in the Semantic Web • Enables the definition of vocabularies for the description of the resources • Goals: • Improved support for interpretation of data by machines • Extensibility, interoperability, and reuse of vocabularies ISU Artificial Intelligence Research Laboratory

  19. The RDF Data Model • Simple but powerful model for creation of metadata • Can be expressed in XML • Consists of three concepts: • Resource: an element, a URI, a literal.. • Properties : directed relations between two resources • Statements : triples of two resources bound by a property • Usual terminology: (s, p, o) subject, predicate, object ISU Artificial Intelligence Research Laboratory

  20. RDF Statement & Graph • Each triple (s, p, o) represents a RDF statement Michael Jackson is the artist of Bad subject (resource) predicate (property) object (resource or literal) http://www.michaeljackson.com http://www.music.org/songs/mj/Bad Artist ISU Artificial Intelligence Research Laboratory

  21. RDF Resource • The Resource forms the central concept in RDF • Anything can be described as a resource (E.g., website, book, picture, persons..) • Resources are identified by URI’s (plus the optional anchor ID’s) http://www.music.org/songs/mj/Bad http://www.michaeljackson.com ISU Artificial Intelligence Research Laboratory

  22. RDF Property • Represents the predicate of an RDF statement • Is labeled with a URI referencing to a RDF property • Is directed pointing from the subject of a statement to the object of a statement http://www.music.org/songs/mj/Bad http://www.michaeljackson.com Artist music:Artist ISU Artificial Intelligence Research Laboratory

  23. Representing RDF documents • RDF Graph Syntax (abstract syntax) • Notation 3 and N-Triples • XML Syntax • XML Serialization Syntax • Abbreviated XML Syntax Variations ISU Artificial Intelligence Research Laboratory

  24. An Example • A person whose name is Michael Jackson and whose homepage is http://www.michaeljackson.com is the artist of http://www.music.org/songs/mj/Bad ISU Artificial Intelligence Research Laboratory

  25. How does RDF help? • Vast majority of data processed by machines can be represented in the form of triples • Subject, Predicate, Object are identified by URI’s • Allows to uniquely identify them • Concepts are notjust words in a document, but are tied to a unique definition found in the Web • Uniqueness is vital to make a consistent statement • Michael Jackson denoted by http://www.michaeljackson.com means the same to everyone ! ISU Artificial Intelligence Research Laboratory

  26. Why is RDF not enough? • RDF properties can be regarded as attributes of resources • RDF properties also represent relationships between resources • But, RDF does not provide mechanisms for describing: • The properties (in terms of their range and domain) • The relationships between the properties and other resources ISU Artificial Intelligence Research Laboratory

  27. Road Map We are here ISU Artificial Intelligence Research Laboratory

  28. RDF(S) – RDF Schema • The RDF Vocabulary Description Language • Enables us to : • Define classes of resources • Define relationships between the classes • Define the kinds of properties that instances of that classes have • Define relationships between properties ISU Artificial Intelligence Research Laboratory

  29. <rdf:Description ID=“ModernMusic"> <rdf:type resource="http://www.w3.org/...#Class"/> <rdfs:subClassOf rdf:resource="http://www.w3.org/...#Resource"/> </rdf:Description> <rdf:Description ID="PopMusic"> <rdf:type resource="http://www.w3.org/...#Class"/> <rdfs:subClassOfrdf:resource="#ModernMusic"/> </rdf:Description> <rdf:Description ID="Artist"> <rdf:type resource="http://www.w3.org/...#Property"/> <rdfs:domainrdf:resource="#PopMusic"/> <rdfs:rangerdf:resource="#Person"/> </rdf:Description> <rdf:Description ID=”hasHomepage"> <rdf:type resource="http://www.w3.org/...#Property"/> <rdfs:subPropertyOfrdf:resource="#Artist"/> </rdf:Description> ISU Artificial Intelligence Research Laboratory

  30. An Introduction to the Semantic Web Part 2: Description Logic and Web Ontology Language (OWL) Jie Bao, AI Lab, Iowa State University Baojie@cs.iastate.edu ISU Artificial Intelligence Research Laboratory

  31. Outline • Introduction & Motivation • XML & RDF • RDFS • OWL • DL • Future look and Resources ISU Artificial Intelligence Research Laboratory

  32. Map You are here ISU Artificial Intelligence Research Laboratory

  33. Problems with RDFS • RDFS too weak to describe resources in sufficient detail • No localised range and domain constraints • Can’t say that the range of hasChild is person when applied to persons and elephant when applied to elephants • No existence/cardinality constraints • Can’t say that all instances of Album have an artist that is also a person, or that albums have at least 1 artist • No transitive, inverse or symmetrical properties • Can’t say that isPartOf is a transitive property, that hasPart is the inverse of isPartOf or that touches is symmetrical • … • Difficult to provide reasoning support • No “native” reasoners for non-standard semantics • May be possible to reason via FO axiomatisation ISU Artificial Intelligence Research Laboratory

  34. Problems with RDFS • RDFS is also too liberal • No distinction between classes and instances (individuals) <Species,type,Class> <Lion,type,Species> <Leo,type,Lion> • Properties can themselves have properties <hasDaughter,type,familyProperty> • No distinction between language constructors and ontology vocabulary, so constructors can be applied to themselves/each other <type,range,Class> <Property,type,Class> <type,subPropertyOf,subClassOf> ISU Artificial Intelligence Research Laboratory

  35. Here comes the ontology • The Tao that can be known is not Tao. The substance of the World is only a name for Tao. - Laozi, Tao Te Ching • Science of Being (Aristotle, Metaphysics, IV, 1) ISU Artificial Intelligence Research Laboratory

  36. Ontology in Computer Science • An ontology is an engineering artifact: • It is constituted by a specific vocabulary used to describe a certain reality, plus • a set of explicit assumptions regarding the intended meaning of the vocabulary. • Thus, an ontology describes a formal specification of a certain domain: • Shared understanding of a domain of interest • Formal and machine manipulable model of a domain of interest “An explicit specification of a conceptualisation” [Gruber93] ISU Artificial Intelligence Research Laboratory

  37. Web Ontology Language Requirements Desirable features identified for Web Ontology Language: • Extends existing Web standards • Such as XML, RDF, RDFS • Easy to understand and use • Should be based on familiar KR idioms • Formally specified • Of “adequate” expressive power • Possible to provide automated reasoning support ISU Artificial Intelligence Research Laboratory

  38. Web Ontology Languages • OIL (Ontology Interface Layer) • Outcome from “On-To-Knowledge” project sponsored by European IST (Information Society Technologies) project • DAML (DARPA Agent Markup Language) • Began as a DARPA research program • DAML-ONT • DAML+OIL • DAML combines OIL components • DAML-S, DAML-L • OWL (Web Ontology Language) • W3C standard • Future: SWRL,… ISU Artificial Intelligence Research Laboratory

  39. Evolution of Web Ontology Languages 1992 1998 1999 2000 2001 2002 2003 Combine vocabularies OIL Define vocabularies Revision XML Extend vocabularies RDF RDFS DAML (DAML+OIL) OWL DAML-ONT SGML For Web services OWL-S Extend HTML tags for semantic description DAML-S HTML SHOE ISU Artificial Intelligence Research Laboratory

  40. OWL • Three species of OWL • OWL full is union of OWL syntax and RDF • OWL DL restricted to FOL fragment (¼ DAML+OIL) • OWL Lite is “easier to implement” subset of OWL DL • Semantic layering • OWL DL ¼ OWL full within DL fragment • DL semantics officially definitive • OWL DL based on SHIQDescription Logic • In fact it is equivalent to SHOIN(Dn) DL • OWL DL Benefits from many years of DL research • Well defined semantics • Formal properties well understood (complexity, decidability) • Known reasoning algorithms • Implemented systems (highly optimised) ISU Artificial Intelligence Research Laboratory

  41. OWL Language Constructs • OWL Classes • Class descriptions • Enumeration • Property restriction:Value constraints , Cardinality constraints • Intersection, union and complement • Class axioms • OWL Properties • RDF Schema property constructs • Relations to other properties • Global cardinality restrictions on properties • Logical characteristics of properties • OWL Individuals • Individual identity • Datatypes • Annotations ISU Artificial Intelligence Research Laboratory

  42. OWL Class Descriptions • Enumeration <owl:Class> <owl:oneOf rdf:parseType="Collection"> <owl:Thing rdf:about="#Eurasia"/> <owl:Thing rdf:about="#Africa"/> <owl:Thing rdf:about="#NorthAmerica"/> <owl:Thing rdf:about="#SouthAmerica"/> <owl:Thing rdf:about="#Australia"/> <owl:Thing rdf:about="#Antarctica"/> </owl:oneOf> </owl:Class> ISU Artificial Intelligence Research Laboratory

  43. OWL Class Descriptions • Property Restriction • Value constraints <owl:Restriction> <owl:onProperty rdf:resource="#hasAlbum" /> <owl:allValuesFrom rdf:resource="#Album" /> </owl:Restriction> <owl:Restriction> <owl:onProperty rdf:resource="#hasAlbum " /> <owl:someValuesFrom rdf:resource="#BestSeller" /> </owl:Restriction> <owl:Restriction> <owl:onProperty rdf:resource="#hasAlbum " /> <owl:hasValue rdf:resource="#Bad" /> </owl:Restriction> ISU Artificial Intelligence Research Laboratory

  44. OWL Class Descriptions • Property Restriction • Cardinality constraints <owl:Restriction> <owl:onProperty rdf:resource="# hasAlbum" /> <owl:maxCardinality>2</owl:maxCardinality> </owl:Restriction> <owl:Restriction> <owl:onProperty rdf:resource="#hasAlbum " /> <owl:minCardinality>2</owl:minCardinality> </owl:Restriction> <owl:Restriction> <owl:onProperty rdf:resource="#hasAlbum " /> <owl:cardinality>2</owl:cardinality> </owl:Restriction> ISU Artificial Intelligence Research Laboratory

  45. OWL Class Descriptions • Intersection <owl:Class> <owl:intersectionOf rdf:parseType="Collection"> <owl:Class> <owl:oneOf rdf:parseType="Collection"> <owl:Thing rdf:about="#Rock" /> <owl:Thing rdf:about="#HeavyMetal" /> </owl:oneOf> </owl:Class> <owl:Class> <owl:oneOf rdf:parseType="Collection"> <owl:Thing rdf:about="#Rap" /> <owl:Thing rdf:about="#Blues" /> </owl:oneOf> </owl:Class> </owl:intersectionOf> </owl:Class> ISU Artificial Intelligence Research Laboratory

  46. OWL Class Descriptions • Union <owl:Class> <owl:unionOf rdf:parseType="Collection"> <owl:Class> <owl:oneOf rdf:parseType="Collection"> <owl:Thing rdf:about="#Rock" /> <owl:Thing rdf:about="#HeavyMetal" /> </owl:oneOf> </owl:Class> <owl:Class> <owl:oneOf rdf:parseType="Collection"> <owl:Thing rdf:about="#Rap" /> <owl:Thing rdf:about="#Blues" /> </owl:oneOf> </owl:Class> </owl:unionOf> </owl:Class> ISU Artificial Intelligence Research Laboratory

  47. OWL Class Descriptions • Complement <owl:Class rdf:ID=“#NonStar”> <owl:complementOf> <owl:Class rdf:about="#Star"/> </owl:complementOf> </owl:Class> ISU Artificial Intelligence Research Laboratory

  48. OWL Class • Subclass <owl:Class rdf:ID=“Rock"> <rdfs:subClassOf rdf:resource="#Music" /> </owl:Class> • Equivalent Class <owl:Class rdf:about="#US_President"> <owl:equivalentClass rdf:resource="#PrincipalResidentOfWhiteHouse"/> </owl:Class> • Disjoint <owl:Class rdf:about="Man"> <owl:disjointWithrdf:resource="#Woman"/> </owl:Class> ISU Artificial Intelligence Research Laboratory

  49. OWL (RDFS) Properties • RDF Schema Property Constructs <owl:ObjectProperty rdf:ID=" hasAlbum"> <rdfs:subPropertyOf rdf:resource="#hasWork"/> </owl:ObjectProperty> • Domain and Range <owl:SymmetricProperty rdf:ID=“hasAlbum"> <rdfs:domainrdf:resource="#Artist"/> <rdfs:rangerdf:resource="#Album"/> </owl:SymmetricProperty> ISU Artificial Intelligence Research Laboratory

  50. OWL Properties • Relations to Other Properties • Equivalent property <owl:ObjectProperty rdf:ID=“hasAlbum"> <owl:equivalentProperty rdf:resource="#hasCD"/> </owl:ObjectProperty> • Inverse <owl:ObjectProperty rdf:ID=“hasAlbum"> <owl:inverseOf rdf:resource="#hasArtist"/> </owl:ObjectProperty> ISU Artificial Intelligence Research Laboratory

More Related