1. Dr. Alexandra I. Cristea http://www.dcs.warwick.ac.uk/~acristea/ CS 253: Topics in Database Systems: C5

2. 2 Previously we looked at: –XML, RDF and its query language(s) Next: –OWL

3. 3 OWL

4. 4 OWL is a Web Standard OWL became a W3C (World Wide Web Consortium) Recommendation in February 2004.

5. 5 What (else) is OWL? OWL stands for Web Ontology Language OWL is built on top of RDF OWL is for processing information on the web OWL was designed to be interpreted by computers OWL was not designed for being read by people OWL is written in XML OWL is a web standard OWL has three sublanguages

6. 6 What is an Ontology? Ontology is about the exact description of things and their relationships and an inference mechanism for it. For the web, ontology is about the exact description of web information and relationships between web information and reasoning with it. dictionary  taxonomy  ontology

7. 7 a philosophical discipline—a branch of philosophy that deals with the nature and the organisation of reality Science of Being (Aristotle, Metaphysics, IV, 1) Tries to answer the questions: What characterizes being? Eventually, what is being? Ontology: Origins and History Ontology in Philosophy

8. 8 Ontology in Linguistics “Tank“ ReferentForm Stands for Relates to activates Concept [Ogden, Richards, 1923] ?

9. 9 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] Ontology in Computer Science

10. 10 The Semantic Web Shared ontologies help to exchange data and meaning between web-based services (Image by Jim Hendler)

11. 11 Wine Example Scenario Tell me what wines I should buy to serve with each course of the following menu. Wine Agent Grocery Agent Books Agent I recommend Chardonney or DryRiesling

12. 12 Ontologies in the Semantic Web Provide shared data structures to exchange information between agents Can be explicitly used as annotations in web sites Can be used for knowledge-based services using other web resources Can help to structure knowledge to build domain models (for other purposes)

13. 13 History of the Semantic Web Web was “invented” by Tim Berners-Lee (amongst others), a physicist working at CERN TBL’s original vision of the Web was much more ambitious than the reality of the existing (syntactic) Web: “... a goal of the Web was that, if the interaction between person and hypertext could be so intuitive that the machine-readable information space gave an accurate representation of the state of people's thoughts, interactions, and work patterns, then machine analysis could become a very powerful management tool, seeing patterns in our work and facilitating our working together through the typical problems which beset the management of large organizations.” TBL (and others) have since been working towards realising this vision, which has become known as the Semantic Web E.g., article in May 2001 issue of Scientific American…

14. 14 Realising the complete “vision” is too hard for now (probably) But we can make a start by adding semantic annotation to web resources Scientific American, May 2001:

15. 15 Where we are Today: the Syntactic Web [Hendler & Miller 02]

16. 16 The Syntactic Web is… A hypermedia, a digital library –A library of documents called (web pages) interconnected by a hypermedia of links A database, an application platform –A common portal to applications accessible through web pages, and presenting their results as web pages A platform for multimedia –BBC Radio 4 anywhere in the world! Terminator 3 trailers! A naming scheme –Unique identity for those documents A place where computers do the presentation (easy) and people do the linking and interpreting (hard). Why not get computers to do more of the hard work? [Goble 03]

17. 17 Hard Work using the Syntactic Web… Find images of Peter Patel-Schneider, Frank van Harmelen and Alan Rector… Rev. Alan M. Gates, Associate Rector of the Church of the Holy Spirit, Lake Forest, Illinois

18. 18 Impossible(?) via the Syntactic Web… Complex queries involving background knowledge –Find information about “animals that use sonar but are not either bats or dolphins” Locating information in data repositories –Travel enquiries –Prices of goods and services –Results of human genome experiments Finding and using “web services” –Visualise surface interactions between two proteins Delegating complex tasks to web “agents” –Book me a holiday next weekend somewhere warm, not too far away, and where they speak French or English, e.g., Barn Owl

19. 19 What is the Problem? Consider a typical web page: Markup consists of: –rendering information (e.g., font size and colour) –Hyper-links to related content Semantic content is accessible to humans but not (easily) to computers…

20. 20 What information can we see… WWW2002 The eleventh international world wide web conference Sheraton waikiki hotel Honolulu, hawaii, USA 7-11 may 2002 1 location 5 days learn interact Registered participants coming from australia, canada, chile denmark, france, germany, ghana, hong kong, india, ireland, italy, japan, malta, new zealand, the netherlands, norway, singapore, switzerland, the united kingdom, the united states, vietnam, zaire Register now On the 7 th May Honolulu will provide the backdrop of the eleventh international world wide web conference. This prestigious event … Speakers confirmed Tim berners-lee

21. 21 What information can a machine see…                   

22. 22 Solution: XML markup with “meaningful” tags?            

23. 23 But What About…           

24. 24 Machine sees…             

25. 25 Need to Add “Semantics” External agreement on meaning of annotations –E.g., Dublin Core Agree on the meaning of a set of annotation tags –Problems with this approach Inflexible Limited number of things can be expressed Use Ontologies to specify meaning of annotations –Ontologies provide a vocabulary of terms –New terms can be formed by combining existing ones –Meaning (semantics) of such terms is formally specified –Can also specify relationships between terms in multiple ontologies

26. 26 Structure of an Ontology Ontologies typically have two distinct components: Names for important concepts in the domain –Elephant is a concept whose members are a kind of animal –Herbivore is a concept whose members are exactly those animals who eat only plants or parts of plants –Adult_Elephant is a concept whose members are exactly those elephants whose age is greater than 20 years Background knowledge/constraints on the domain –Adult_Elephants weigh at least 2,000 kg –All Elephants are either African_Elephants or Indian_Elephants –No individual can be both a Herbivore and a Carnivore

27. 27 Example Ontology

28. 28 A Semantic Web — First Steps Extend existing rendering markup with semantic markup –Metadata annotations that describe content/function of web accessible resources Use Ontologies to provide vocabulary for annotations –“Formal specification” is accessible to machines A prerequisite is a standard web ontology language –Need to agree common syntax before we can share semantics –Syntactic web based on standards such as HTTP and HTML Make web resources more accessible to automated processes

29. 29 Ontology Design and Deployment Given key role of ontologies in the Semantic Web, it will be essential to provide tools and services to help users: –Design and maintain high quality ontologies, e.g.: Meaningful — all named classes can have instances Correct — captured intuitions of domain experts Minimally redundant — no unintended synonyms Richly axiomatised — (sufficiently) detailed descriptions –Store (large numbers) of instances of ontology classes, e.g.: Annotations from web pages –Answer queries over ontology classes and instances, e.g.: Find more general/specific classes Retrieve annotations/pages matching a given description –Integrate and align multiple ontologies (merging)

30. Ontology Languages for the Semantic Web

31. 31 Ontology Languages Wide variety of languages for “Explicit Specification” –Graphical notations Semantic networks Topic Maps (see http://www.topicmaps.org/) UML RDF –Logic based Description Logics (e.g., OIL, DAML+OIL, OWL) Rules (e.g., RuleML, Prolog) First Order Logic (e.g., KIF) Conceptual graphs (Syntactically) higher order logics (e.g., LBase) Non-classical logics (e.g., Flogic, Non-Mon, modalities) –Probabilistic/fuzzy Degree of formality varies widely –Increased formality makes languages more amenable to machine processing (e.g., automated reasoning)

32. 32 Objects/Instances/Individuals –Elements of the domain of discourse –Equivalent to constants in FOL Types/Classes/Concepts –Sets of objects sharing certain characteristics –Equivalent to unary predicates in FOL Relations/Properties/Roles –Sets of pairs (tuples) of objects –Equivalent to binary predicates in FOL Such languages are/can be: –Well understood –Formally specified –(Relatively) easy to use –Amenable to machine processing Many languages use “OO” model based on:

33. 33 Web “Schema” Languages Existing Web languages extended to facilitate content description –XML  XML Schema (XMLS) –RDF  RDF Schema (RDFS) XMLS not an ontology language –Changes format ~ DTDs (document schemas) for XML –Adds an extensible type hierarchy Integers, Strings, etc. Can define sub-types, e.g., positive integers RDFS is recognisable as an ontology language –Classes and properties –Sub/super-classes (and properties) –Range and domain (of properties)

34. 34 Why OWL? OWL is a part of the "Semantic Web Vision" - a future where: Web information has exact meaning Web information can be processed by computers Computers can integrate information from the web

35. 35 OWL was designed for Processing Information OWL was designed to provide a common way to process the content of web information (instead of displaying it). OWL was designed to be read by computer applications (instead of humans).

36. 36 OWL is Different from RDF OWL, RDF similar but OWL –stronger language –greater machine interpretability –larger vocabulary –stronger syntax.

37. 37 OWL Sublanguages OWL has three sublanguages: –OWL Lite hierarchy + simple constraints + cardinality {0,1} –OWL DL (includes OWL Lite) complete, decidable Type separations (class <> property <> individual) –OWL Full (includes OWL DL) aug. meaning RDF..

38. 38 OWL is Written in XML By using XML, OWL information can easily be exchanged between different types of computers using different types of operating system and application languages. Oh yes, there is a namespace: xmlns:owl ="http://www.w3.org/2002/07/owl#"

39. 39 (more on) OWL Based on predecessors (DAML+OIL) A Web Language: Based on RDF(S) An Ontology Language: Based on logic

40. 40 OWL Ontologies What’s inside an OWL ontology –Classes + class-hierarchy –Properties (Slots) / values –Relations between classes (inheritance, disjoints, equivalents) –Restrictions on properties (type, cardinality) –Characteristics of properties (transitive, …) –Annotations –Individuals Reasoning tasks: classification, consistency checking

41. 41 OWL Use Cases At least two different user groups –OWL used as data exchange language (define interfaces of services and agents) –OWL used for terminologies or knowledge models OWL DL is the subset of OWL (Full) that is optimized for reasoning and knowledge modeling

42. 42 OWL Example (Airport) OWL Resource: http://www.daml.org/2001/10/html/airport-onthttp://www.daml.org/2001/10/html/airport-ont Class: AirportAirport Properties: elevation iataCode icaoCode latitude location longitude name Produced using dumpont2.javadumpont2.java

43. 43 Scenario Semantic Web for Tourism/Traveling Goal: Find matching holiday destinations for a customer I am looking for a comfortable destination with beach access Tourism Web

44. 44 Scenario Architecture A search problem: Match customer’s expectations with potential destinations Required: Web Service that exploits formal information about the available destinations –Accommodation (Hotels, B&B, Camping,...) –Activities (Sightseeing, Sports,...)

45. 45 Tourism Semantic Web Open World: –New hotels are being added –New activities are offered Providers publish their services dynamically Standard format / grounding is needed → Tourism Ontology

46. 46 Tourism Semantic Web OWL Metadata (Individuals) OWL Metadata (Individuals) OWL Metadata (Individuals) OWL Metadata (Individuals) Tourism Ontology Web Services Destination AccomodationActivity

47. 47 OWL (in Protégé) Individuals (e.g., “FourSeasons”) Properties – ObjectProperties (references) – DatatypeProperties (simple values) Classes (e.g., “Hotel”)

48. 48 Individuals (Instances) Represent objects in the domain Specific things Two names could represent the same “real-world” individual SydneysOlympicBeach BondiBeach Sydney

49. 49 Example of Individuals equivalent to:

50. 50 ObjectProperties Link two individuals together Relationships (0..n, n..m) Sydney BondiBeach hasPart FourSeasons hasAccomodation

51. 51 Example Property

52. 52 Property Domain & Range If a relation is: subject_individual  hasProperty  object_individual The domain is the class of the subject individual The range is the class of the object individual (or a datatype if hasProperty is a Datatype Property) DomainClassRangeClass hasProperty

53. 53 Example Cardinality 1... 1

54. 54 Inverse Properties Represent bidirectional relationships Adding a value to one property also adds a value to the inverse property (!) Sydney BondiBeach hasPart isPartOf

55. 55 Inverse Property Example

56. 56 Transitive Properties If A is related to B and B is related to C then A is also related to C Often used for part-of relationships Sydney BondiBeach hasPart NewSouthWales hasPart hasPart (derived)

57. 57 Transitive Property Example

58. 58 Sub-properties Example...

59. 59 DatatypeProperties Link individuals to primitive values (integers, floats, strings, booleans etc) Often: AnnotationProperties without formal “meaning” Sydney hasSize = 4,500,000 isCapital = true rdfs:comment = “Don’t miss the opera house”

60. 60 Classes Sets of individuals with common characteristics Individuals are instances of at least one class City Sydney Beach Cairns BondiBeach CurrawongBeach

61. 61 Examples of Classes in OWL

62. 62 Range and Domain Property characteristics –Domain: “left side of relation” (Destination) –Range: “right side” (Accomodation) Sydney BestWestern FourSeasons hasAccomodation Destination Accomodation hasAccomodation

63. 63 Example Propery, Domain & Range

64. 64 Domains Individuals can only take values of properties that have matching domain –“Only Destinations can have Accommodations” Domain can contain multiple classes Domain can be undefined: Property can be used everywhere

65. 65 Superclass Relationships Classes can be organized in a hierarchy Direct instances of subclass are also (indirect) instances of superclasses Cairns Sydney Canberra Coonabarabran

66. 66 Example Subclasses … wine vin...

67. 67 Class Relationships Classes can overlap arbitrarily City Sydney Cairns BondiBeach RetireeDestination

68. 68 Class Disjointness All classes could potentially overlap In many cases we want to make sure they don’t share instances Sydney UrbanAreaRuralArea Sydney Woomera CapeYork disjointWith City Destination

69. 69 Example disjoint

70. 70 Class versus Individual (Instance) Levels of representation: –In certain contexts a class can be considered an instance of something else. –Grape, set of all grape varietals. CabernetSauvingonGrape is an instance of this class, but could be considered a class, the set of all actual Cabernet Sauvignon grapes. Subclass vs. instance: easy to confuse instance-of relationship with subclass relationship! –CabernetSauvignonGrape as individual & instance of Grape, or subclass of Grape. –But: Grape class is the set of all grape varietals, any subclass should be a subset. –CabernetSauvignonGrape is an instance of Grape, It does not describe a subset of Grape varietals, it is a grape varietal.

71. 71 Class Descriptions Classes can be described by their logical characteristics Descriptions are “anonymous classes” Things with three star accomodation Things with sightseeing opportunities RetireeDestination Sydney SanJose BlueMountains

72. 72 Class Descriptions Define the “meaning” of classes Anonymous class expressions are used –“All national parks have campgrounds.” –“A backpackers destination is a destination that has budget accommodation and offers sports or adventure activities.” Expressions mostly restrict property values (OWL Restrictions)

73. 73 Class Descriptions: Why? Based on OWL’s Description Logic support Formalize intentions and modeling decisions (comparable to test cases) Make sure that individuals fulfill conditions Tool-supported reasoning

74. 74 Reasoning with Classes Tool support for 3 types of reasoning exists: –Consistency checking: Can a class have any instances? –Classification: Is A a subclass of B? –Instance classification: Which classes does an individual belong to?

75. 75 Restrictions (Overview) Define a condition for property values – allValuesFrom – someValuesFrom – hasValue – minCardinality – maxCardinality – cardinality An anonymous class consisting of all individuals that fulfill the condition

76. 76 Cardinality Restrictions Meaning: The property must have at least/at most/exactly x values is the shortcut for and Example: A FamilyDestination is a Destination that has at least one Accomodation and at least 2 Activities

77. 77 allValuesFrom Restrictions Meaning: All values of the property must be of a certain type Warning: Also individuals with no values fulfill this condition (trivial satisfaction) Example: Hiking is a Sport that is only possible in NationalParks

78. 78 Value constraints

79. 79 someValuesFrom Restrictions Meaning: At least one value of the property must be of a certain type Others may exist as well Example: A NationalPark is a RuralArea that has at least one Campground and offers at least one Hiking opportunity

80. 80 hasValue Restrictions Meaning: At least one of the values of the property is a certain value Similar to someValuesFrom but with Individuals and primitive values Example: A PartOfSydney is a Destination where one of the values of the isPartOf property is Sydney

81. 81 Enumerated Classes Consist of exactly the listed individuals OneStarRating TwoStarRating ThreeStarRating BudgetAccomodation

82. 82 Example Description: Enumeration

83. 83 Logical Class Definitions Define classes out of other classes – unionOf (or) – intersectionOf (and) – complementOf (not) Allow arbitrary nesting of class descriptions (A and (B or C) and not D)

84. 84 unionOf The class of individuals that belong to class A or class B (or both) Example: Adventure or Sports activities AdventureSports

85. 85 intersectionOf The class of individuals that belong to both class A and class B Example: A BudgetHotelDestination is a destination with accomodation that is a budget accomodation and a hotel BudgetAccomodation Hotel

86. 86 Implicit intersectionOf When a class is defined by more than one class description, then it consists of the intersection of the descriptions Example: A luxury hotel is a hotel that is also an accommodation with 3 stars AccomodationWith3Stars Hotel LuxuryHotel

87. 87 complementOf The class of all individuals that do not belong to a certain class Example: A quiet destination is a destination that is not a family destination Destination FamilyDestination QuietDestination (grayed)

88. 88 Class Conditions Necessary Conditions: (Primitive / partial classes) “If we know that something is a X, then it must fulfill the conditions...” Necessary & Sufficient Conditions: (Defined / complete classes) “If something fulfills the conditions..., then it is an X.”

89. 89 Class Conditions (2) QuietDestination NationalPark (not everything that fulfills these conditions is a NationalPark) (everything that fulfills these conditions is a QuietDestination)

90. 90 Classification NationalPark BackpackersDestination A RuralArea is a Destination A Campground is BudgetAccomodation Hiking is a Sport Therefore: Every NationalPark is a Backpackers-Destination (Other BackpackerDestinations)

91. 91 Visualization with OWLViz

92. 92 Putting it All Together Ontology has been developed Published on a dedicated web address Ontology provides standard terminology Other ontologies can extend it Users can instantiate the ontology to provide instances –specific hotels –specific activities

93. 93 Ontology Import Adds all classes, properties and individuals from an external OWL ontology into your project Allows to create individuals, subclasses, or to further restrict imported classes Can be used to instantiate an ontology for the Semantic Web

94. 94 Tourism Semantic Web (2) OWL Metadata (Individuals) Tourism Ontology Web Services Destination AccommodationActivity

95. 95 OWL File <rdf:RDF xmlns="http://protege.stanford.edu/plugins/owl/owl- library/heli-bunjee.owl#" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xmlns:owl="http://www.w3.org/2002/07/owl#" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:travel="http://protege.stanford.edu/plugins/ owl/owl-library/travel.owl#" xml:base="http://protege.stanford.edu/plugins/owl/owl- library/heli-bunjee.owl"> [...]

96. 96 OWL File: [...] OWL body in RDF wrap <owl:imports rdf:resource="http://protege.stanford.edu/ plugins/owl/owl-library/travel.owl"/> <rdfs:subClassOf rdf:resource="http://protege.stanford. edu/plugins/owl/owl-library/travel.owl#BunjeeJumping"/> [***]

97. 97 OWL File [***] in HeliBunjeeJumping [ +++ ] Manic super bunjee now offers nerve wrecking jumps from 300 feet right out of a helicopter. Satisfaction guaranteed.

98. 98 OWL File [+++] in travel:hasContact msb@manicsuperbunjee. com Sydney Queen Victoria St 1240

99. 99 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 person have a mother that is also a person, or that persons have exactly 2 parents –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

100. 100 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

101. 101 From RDF to OWL Two languages developed to satisfy above requirements –OIL: developed by group of (largely) European researchers (several from EU OntoKnowledge project) –DAML-ONT: developed by group of (largely) US researchers (in DARPA DAML programme) Efforts merged to produce DAML+OIL –Development was carried out by “Joint EU/US Committee on Agent Markup Languages” –Extends (“DL subset” of) RDF DAML+OIL submitted to W3C as basis for standardisation –Web-Ontology (WebOnt) Working Group formed –WebOnt group developed OWL language based on DAML+OIL –OWL language now a W3C Proposed Recommendation

102. 102 OWL Language 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 SHIQ Description Logic –In fact it is equivalent to SHOIN (D n ) 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)

103. 103 (In)famous “Layer Cake”  Data Exchange  Semantics+reasoning  Relational Data ? ? ??? Relationship between layers is not clear OWL DL extends “DL subset” of RDF

104. 104 OWL built-in classes owl:FunctionalProperty, owl:InverseFunctionalProperty, owl:SymmetricProperty, owl:TransitiveProperty, owl:DeprecatedClass, owl:DeprecatedProperty

105. 105 OWL built in properties owl:equivalentClass, owl:disjointWith, owl:equivalentProperty, owl:inverseOf, owl:sameAs, owl:differentFrom, owl:complementOf, owl:unionOf, owl:intersectionOf, owl:oneOf, owl:allValuesFrom, owl:onProperty, owl:someValuesFrom, owl:hasValue, owl:minCardinality, owl:maxCardinality, owl:cardinality, owl:distinctMembers annotation properties: owl:versionInfo, rdfs:label, rdfs:comment, rdfs:seeAlso, rdfs:isDefinedBy ontology properties: owl:imports, owl:priorVersion, owl:backwardCompatibleWith, owl:incompatibleWith

106. 106 OWL Class Constructors XMLS datatypes as well as classes in Arbitrarily complex nesting of constructors

107. 107 RDFS Syntax E.g., Person hasChild.Doctor hasChild.Doctor:

108. 108 OWL Axioms Axioms (mostly) reducible to inclusion (v) –C ´ D iff both C v D and D v C

109. 109 XML Schema Datatypes in OWL OWL supports XML Schema primitive datatypes –E.g., integer, real, string, … Strict separation between “object” classes and datatypes –Disjoint interpretation domain for datatypes –Disjoint “object” and datatype properties

110. 110 Why Separate Classes and Datatypes? Philosophical reasons: –Datatypes structured by built-in predicates –Not appropriate to form new datatypes using ontology language Practical reasons: –Ontology language remains simple and compact –Semantic integrity of ontology language not compromised –Implementability not compromised — can use hybrid reasoner

111. 111 OWL query language: OWL-QL OWL Query Language (OWL-QL) is an updated version of the DAML Query Language (DQL).OWL Query Language (OWL-QL) It is intended to be a candidate standard language and protocol for query- answering dialogues among Semantic Web computational agents.

112. 112 Acknowledgements Thanks to various people from whom I “borrowed” material: –Jeen Broekstra –Carole Goble –Frank van Harmelen –Austin Tate –Raphael Volz And thanks to all the people from whom they borrowed it

113. 113 OWL Conclusion We have learned: –OWL definition –OWL comparison with RDF –OWL classes and properties –Usage scenarios