2. The Semantic Web SDBI 2010 Most of the slides are book slides of Semantic Web Primer, by Grigoris Antoniou and Frank van Harmelen

3. Talk Structure The Vision of the Semantic Web The Main Technologies Interesting Aspects/Research Issues

4. The Vision

5. The Semantic Web in 1 Slide The Web = Linked Documents The Semantic Web = Linked Data Turning Documents into Knowledge!

6. Chapter 1A Semantic Web Primer 6 Today’s Web Most of today’s Web content is suitable for human consumption – Even Web content that is generated automatically from databases is usually presented without the original structural information found in databases Typical Web uses today people’s – seeking and making use of information, searching for and getting in touch with other people, reviewing catalogs of online stores and ordering products by filling out forms

7. Chapter 1A Semantic Web Primer 7 Keyword-Based Search Engines Current Web activities are not particularly well supported by software tools – Except for keyword-based search engines (e.g. Google, AltaVista, Yahoo) The Web would not have been the huge success it was, were it not for search engines

8. Chapter 1A Semantic Web Primer 8 Problems of Keyword-Based Search Engines Results are highly sensitive to vocabulary Results are single Web pages Human involvement is necessary to interpret and combine results Results of Web searches are not readily accessible by other software tools

9. Chapter 1A Semantic Web Primer 9 The Key Problem of Today’s Web The meaning of Web content is not machine- accessible: lack of semantics It is simply difficult to distinguish the meaning between these two sentences: I am a professor of computer science. I am a professor of computer science, you may think. Well,...

10. Chapter 1A Semantic Web Primer 10 The Semantic Web Approach Represent Web content in a form that is more easily machine-processable. Use intelligent techniques to take advantage of these representations. The Semantic Web will gradually evolve out of the existing Web, it is not a competition to the current WWW

11. Chapter 1A Semantic Web Primer 11 Semantic Web Enabled Knowledge Management Knowledge will be organized in conceptual spaces according to its meaning. Automated tools for maintenance and knowledge discovery Semantic query answering Query answering over several documents Defining who may view certain parts of information (even parts of documents) will be possible.

13. Example Scenario You want to attend an out-of-town conference Physical address of conference appears on the conference Web site You copy its physical address from the site and go to Hotels.com, to find a nearby hotel You get a list of hotels and go to MapQuest, and enter hotel addresses and the conference center address to see which hotels are close to the conference center. Did you notice: COPY, PASTE, CLICK, COPY, PASTE, CLICK, again and again…

14. Can Such Processes be Automated? Yes, if all sites agree on how to represent an address Even better, if the data was linked, and we could browse via the address to related properties…

15. Even a Little bit of Semantics Goes a Long Way Even just having semantic marking of values, such as time, dates, locations, zipcodes, currency, etc, is of great value Example applications (Tim Berners-Lee) – Integrate address book and map – Integrate photos with calendar

16. The Main Technologies

17. Enabling Technologies XML RDF, SPARQL OWL

18. Chapter 2A Semantic Web Primer 18 An HTML Example Nonmonotonic Reasoning: Context- Dependent Reasoning by V. Marek and M. Truszczynski Springer 1993 ISBN 0387976892

19. Chapter 2A Semantic Web Primer 19 The Same Example in XML Nonmonotonic Reasoning: Context- Dependent Reasoning V. Marek M. Truszczynski Springer 1993 0387976892

20. Chapter 2A Semantic Web Primer 20 HTML versus XML: Similarities Both use tags (e.g. and ) Tags may be nested (tags within tags) Human users can read and interpret both HTML and XML representations quite easily … But how about machines?

21. Chapter 2A Semantic Web Primer 21 Problems with Automated Interpretation of HTML Documents An intelligent agent trying to retrieve the names of the authors of the book Authors’ names could appear immediately after the title or immediately after the word by Are there two authors? Or just one, called “V. Marek and M. Truszczynski”?

22. Chapter 2A Semantic Web Primer 22 HTML vs XML: Structural Information HTML documents do not contain structural information: pieces of the document and their relationships. XML more easily accessible to machines because – Every piece of information is described. – Relations are also defined through the nesting structure. – E.g., the tags appear within the tags, so they describe properties of the particular book.

23. Chapter 2A Semantic Web Primer 23 HTML vs XML: Structural Information (2) A machine processing the XML document would be able to deduce that – the author element refers to the enclosing book element – rather than by proximity considerations XML allows the definition of constraints on values – E.g. a year must be a number of four digits

24. Chapter 2A Semantic Web Primer 24 HTML vs XML: Formatting The HTML representation provides more than the XML representation: – The formatting of the document is also described Τhe main use of an HTML document is to display information: it must define formatting XML: separation of content from display – same information can be displayed in different ways

25. Chapter 2A Semantic Web Primer 25 HTML vs XML: Another Example In HTML Relationship force-mass F = M × a In XML Relationship force-mass F M × a

26. Chapter 2A Semantic Web Primer 26 HTML vs XML: Different Use of Tags In both HTML docs same tags In XML completely different HTML tags define display: color, lists … XML tags not fixed: user definable tags XML meta markup language: language for defining markup languages

27. Chapter 2A Semantic Web Primer 27 XML Vocabularies Web applications must agree on common vocabularies to communicate and collaborate Communities and business sectors are defining their specialized vocabularies – mathematics (MathML) – bioinformatics (BSML) – human resources (HRML) – …

28. Chapter 2A Semantic Web Primer 28 Summary XML is a metalanguage that allows users to define markup XML separates content and structure from formatting XML is the de facto standard for the representation and exchange of structured information on the Web XML is supported by query languages

29. Enabling Technologies XML RDF, SPARQL OWL

30. Chapter 3A Semantic Web Primer 30 Drawbacks of XML XML is a universal metalanguage for defining markup It provides a uniform framework for interchange of data and metadata between applications However, XML does not provide any means of talking about the semantics (meaning) of data E.g., there is no intended meaning associated with the nesting of tags – It is up to each application to interpret the nesting.

31. Chapter 3A Semantic Web Primer 31 Nesting of Tags in XML David Billington is a lecturer of Discrete Maths David Billington Discrete Maths Opposite nesting, same information!

32. Can’t we just use XML? This is what a web-page in natural language looks like for a machine

33. XML helps CV name education work private XML allows “meaningful tags” to be added to parts of the text

34. XML  machine accessible meaning CV name education work private But to your machine, the tags look like this….

35. Schemas take a step in the right direction Schemas help…. …by relating common terms between documents 

36. But other people use other schemas CV name education work private   >  Someone else has one like this….

37. The “semantics” isn’t there …which don’t fit in  RDF is the FIRST step to solve this problem

38. Chapter 3A Semantic Web Primer 38 Basic Ideas of RDF Basic building block: object-attribute-value triple – It is called a statement – Sentence about Billington is such a statement RDF has been given a syntax in XML – This syntax inherits the benefits of XML – Other syntactic representations of RDF possible

39. Chapter 3A Semantic Web Primer 39 Basic Ideas of RDF (2) The fundamental concepts of RDF are: – resources – properties – statements

40. Chapter 3A Semantic Web Primer 40 Resources We can think of a resource as an object, a “thing” we want to talk about – E.g. authors, books, publishers, places, people, hotels Every resource has a URI, a Universal Resource Identifier A URI can be – a URL (Web address) or – some other kind of unique identifier

41. Chapter 3A Semantic Web Primer 41 Properties Properties are a special kind of resources They describe relations between resources – E.g. “written by”, “age”, “title”, etc. Properties are also identified by URIs Advantages of using URIs: – Α global, worldwide, unique naming scheme – Reduces the homonym problem of distributed data representation

42. Chapter 3A Semantic Web Primer 42 Statements Statements assert the properties of resources A statement is an object-attribute-value triple – It consists of a resource, a property, and a value Values can be resources or literals – Literals are atomic values (strings)

43. Chapter 3A Semantic Web Primer 43 Three Views of a Statement A triple A piece of a graph A piece of XML code Thus an RDF document can be viewed as: A set of triples A graph (semantic net) An XML document

44. Chapter 3A Semantic Web Primer 44 Statements as Triples (http://www.cit.gu.edu.au/~db, http://www.mydomain.org/site-owner, #David Billington) The triple (x,P,y) can be considered as a logical formula P(x,y) – Binary predicate P relates object x to object y – RDF offers only binary predicates (properties)

45. Chapter 3A Semantic Web Primer 45 A Piece of a Graph A directed graph with labeled nodes and arcs – from the resource (the subject of the statement) – to the value (the object of the statement) Known in AI as a semantic net The value of a statement may be a resource – Ιt may be linked to other resources

46. Chapter 3A Semantic Web Primer 46 A Set of Triples as a Semantic Net

47. Chapter 3A Semantic Web Primer 47 Statements in XML Syntax Graphs are a powerful tool for human understanding but The Semantic Web vision requires machine- accessible and machine-processable representations There is a 3rd representation based on XML – But XML is not a part of the RDF data model – E.g. serialisation of XML is irrelevant for RDF

48. Chapter 3A Semantic Web Primer 48 Statements in XML (2) <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:mydomain="http://www.mydomain.org/my-rdf-ns"> <rdf:Description rdf:about="http://www.cit.gu.edu.au/~db"> <mydomain:site-owner rdf:resource=“#David Billington“/>

49. Chapter 3A Semantic Web Primer 49 Reification In RDF it is possible to make statements about statements – Grigoris believes that David Billington is the creator of http://www.cit.gu.edu.au/~db Such statements can be used to describe belief or trust in other statements The solution is to assign a unique identifier to each statement – It can be used to refer to the statement

50. Chapter 3A Semantic Web Primer 50 Data Types Data types are used in programming languages to allow interpretation In RDF, typed literals are used, if necessary (#David Billington, http://www.mydomain.org/age, “27”^http://www.w3.org/2001/XMLSchema #integer)

51. Chapter 3A Semantic Web Primer 51 A Critical View of RDF: Binary Predicates RDF uses only binary properties – This is a restriction because often we use predicates with more than 2 arguments – But binary predicates can simulate these Example: referee(X,Y,Z) – X is the referee in a chess game between players Y and Z

52. Chapter 3A Semantic Web Primer 52 A Critical View of RDF: Binary Predicates (2) We introduce: – a new auxiliary resource chessGame – the binary predicates ref, player1, and player2 We can represent referee(X,Y,Z) as:

53. Chapter 3A Semantic Web Primer 53 Basic Ideas of RDF Schema RDF is a universal language that lets users describe resources in their own vocabularies – RDF does not assume, nor does it define semantics of any particular application domain The user can do so in RDF Schema using: – Classes and Properties – Class Hierarchies and Inheritance – Property Hierarchies

54. Chapter 3A Semantic Web Primer 54 Classes and their Instances We must distinguish between – Concrete “things” (individual objects) in the domain: Discrete Maths, David Billington etc. – Sets of individuals sharing properties called classes: lecturers, students, courses etc. Individual objects that belong to a class are referred to as instances of that class The relationship between instances and classes in RDF is through rdf:type

55. Chapter 3A Semantic Web Primer 55 Why Classes are Useful Impose restrictions on what can be stated in an RDF document using the schema – As in programming languages – E.g. A+1, where A is an array – Disallow nonsense from being stated

56. Chapter 3A Semantic Web Primer 56 Nonsensical Statements disallowed through the Use of Classes Discrete Maths is taught by Concrete Maths – We want courses to be taught by lecturers only – Restriction on values of the property “is taught by” (range restriction) Room MZH5760 is taught by David Billington – Only courses can be taught – This imposes a restriction on the objects to which the property can be applied (domain restriction)

57. Chapter 3A Semantic Web Primer 57 Class Hierarchies Classes can be organised in hierarchies – A is a subclass of B if every instance of A is also an instance of B – Then B is a superclass of A A subclass graph need not be a tree A class may have multiple superclasses

58. Chapter 3A Semantic Web Primer 58 Class Hierarchy Example

59. Chapter 3A Semantic Web Primer 59 Property Hierarchies Hierarchical relationships for properties – E.g., “is taught by” is a subproperty of “involves” – If a course C is taught by an academic staff member A, then C also involves Α The converse is not necessarily true – E.g., A may be the teacher of the course C, or – a tutor who marks student homework but does not teach C P is a subproperty of Q, if Q(x,y) is true whenever P(x,y) is true

60. Chapter 3A Semantic Web Primer 60 Semantics based on Inference Rules Semantics defined in terms of RDF triples (can also be defined in terms of first-order logic) This inference system consists of inference rules of the form: IF E contains certain triples THEN add to E certain additional triples where E is an arbitrary set of RDF triples

61. Chapter 3A Semantic Web Primer 61 Examples of Inference Rules IF E contains the triple (?x,?p,?y) THEN E also contains (?p,rdf:type,rdf:property) IF E contains the triples (?u,rdfs:subClassOf,?v) and (?v,rdfs:subclassOf,?w) THEN E also contains the triple (?u,rdfs:subClassOf,?w) IF E contains the triples (?x,rdf:type,?u) and (?u,rdfs:subClassOf,?v) THEN E also contains the triple (?x,rdf:type,?v)

62. Is there RDF on the Web? http://esw.w3.org/SweoIG/TaskForces/CommunityProjects/LinkingOpenData Yes, over 25 billion triples!

63. Some of the Topics currently in RDF Great, there is a lot of data out there! Now, how do we query it? – SPARQL

64. SPARQL Basic Queries SPARQL is based on matching graph patterns The simplest graph pattern is the triple pattern : - like an RDF triple, but with the possibility of a variable instead of an RDF term in the subject, predicate, or object positions Combining triple patterns gives a basic graph pattern, where an exact match to a graph is needed to fulfill a pattern

65. Examples PREFIX rdf: PREFIX rdfs: SELECT ?c WHERE { ?c rdf:type rdfs:Class. } Retrieves all triple patterns, where: -the property is rdf:type -the object is rdfs:Class Which means that it retrieves all classes

66. Examples (2) Get all instances of a particular class (e.g. course) : (declaration of rdf, rdfs prefixes omitted for brevity) PREFIX uni: SELECT ?i WHERE { ?i rdf:type uni:course. }

67. Examples (3) SELECT ?x ?y WHERE { ?x rdf:type uni:Lecturer. ?x uni:phone ?y. }

68. Chapter 3A Semantic Web Primer 68 Summary RDF provides a foundation for representing and processing metadata RDF has a graph-based data model RDF has an XML-based syntax to support syntactic interoperability – XML and RDF complement each other because RDF supports semantic interoperability RDF has a decentralized philosophy and allows incremental building of knowledge, and its sharing and reuse

69. Chapter 3A Semantic Web Primer 69 Summary (2) RDF is domain-independent - RDF Schema provides a mechanism for describing specific domains RDF Schema is a primitive ontology language – It offers certain modelling primitives with fixed meaning Key concepts of RDF Schema are class, subclass relations, property, subproperty relations, and domain and range restrictions There exist query languages for RDF and RDFS, including SPARQL

70. Enabling Technologies XML RDF, SPARQL OWL

71. Chapter 4A Semantic Web Primer 71 Requirements for Ontology Languages Ontology languages allow users to write explicit, formal conceptualizations of domain models The main requirements are: – a well-defined syntax – efficient reasoning support – a formal semantics – sufficient expressive power – convenience of expression

72. Chapter 4A Semantic Web Primer 72 Tradeoff between Expressive Power and Efficient Reasoning Support The richer the language is, the more inefficient the reasoning support becomes Sometimes it crosses the border of noncomputability We need a compromise: – A language supported by reasonably efficient reasoners – A language that can express large classes of ontologies and knowledge.

73. Chapter 4A Semantic Web Primer 73 Reasoning About Knowledge in Ontology Languages Class membership – If x is an instance of a class C, and C is a subclass of D, then we can infer that x is an instance of D Equivalence of classes – If class A is equivalent to class B, and class B is equivalent to class C, then A is equivalent to C, too

74. Chapter 4A Semantic Web Primer 74 Reasoning About Knowledge in Ontology Languages (2) Consistency – X instance of classes A and B, but A and B are disjoint – This is an indication of an error in the ontology Classification – Certain property-value pairs are a sufficient condition for membership in a class A; if an individual x satisfies such conditions, we can conclude that x must be an instance of A

75. Chapter 4A Semantic Web Primer 75 Uses for Reasoning Reasoning support is important for – checking the consistency of the ontology and the knowledge – checking for unintended relationships between classes – automatically classifying instances in classes Checks like the preceding ones are valuable for – designing large ontologies, where multiple authors are involved – integrating and sharing ontologies from various sources

76. Chapter 4A Semantic Web Primer 76 Owl Example: Inverse Properties

77. Chapter 4A Semantic Web Primer 77 Owl Example: Equivalent Properties owl:equivalentProperty

78. Chapter 4A Semantic Web Primer 78 Owl Example: owl:allValuesFrom

79. Chapter 4A Semantic Web Primer 79 Owl Example: Special Properties

80. Chapter 4A Semantic Web Primer 80 Reasoning Support for OWL Semantics is a prerequisite for reasoning support Formal semantics and reasoning support are usually provided by – mapping an ontology language to a known logical formalism – using automated reasoners that already exist for those formalisms OWL is (partially) mapped on a description logic Description logics are a subset of predicate logic for which efficient reasoning support is possible

81. Chapter 4A Semantic Web Primer 81 Combining OWL with RDF Schema Ideally, OWL would extend RDF Schema – Consistent with the layered architecture of the Semantic Web But simply extending RDF Schema would work against obtaining expressive power and efficient reasoning – Combining RDF Schema with logic leads to uncontrollable computational properties

82. Chapter 4A Semantic Web Primer 82 Three Species of OWL W3C’sWeb Ontology Working Group defined OWL as three different sublanguages: – OWL Full – OWL DL – OWL Lite Each sublanguage geared toward fulfilling different aspects of requirements

83. Chapter 4A Semantic Web Primer 83 OWL Full It uses all the OWL languages primitives It allows the combination of these primitives in arbitrary ways with RDF and RDF Schema OWL Full is fully upward-compatible with RDF, both syntactically and semantically OWL Full is so powerful that it is undecidable – No complete (or efficient) reasoning support

84. Chapter 4A Semantic Web Primer 84 OWL DL OWL DL (Description Logic) is a sublanguage of OWL Full that restricts application of the constructors from OWL and RDF – Application of OWL’s constructors’ to each other is disallowed – Therefore it corresponds to a well studied description logic OWL DL permits efficient reasoning support

85. Chapter 4A Semantic Web Primer 85 OWL Lite An even further restriction limits OWL DL to a subset of the language constructors – E.g., OWL Lite excludes enumerated classes, disjointness statements, and arbitrary cardinality. The advantage of this is a language that is easier to – grasp, for users – implement, for tool builders The disadvantage is restricted expressivity

86. How Should Reasoning be Done? Open-world assumption We cannot conclude some statement x to be false simply because we cannot show x to be true Our axioms may be simply noncommittal on the status of x We may not deduce falsity from the absence of truth Chapter 4A Semantic Web Primer 86

87. How Should Reasoning be Done? Unique-name assumption (UNA) When two individuals are known by different names, they are in fact different individuals This is an assumption that sometimes works (ex. Product codes) and sometimes doesn’t (ex. Social environment) OWL does not make the unique-name assumption Chapter 4A Semantic Web Primer 87

88. Chapter 5A Semantic Web Primer 88 How Should Reasoning be Done? Monotonic vs. Non-monotonic Rules Example: An online vendor wants to give a special discount if it is a customer’s birthday Define these with OWL (or extension) rules Solution 1 R1: If birthday, then special discount R2: If not birthday, then not special discount But what happens if a customer refuses to provide his birthday due to privacy concerns?

89. Chapter 5A Semantic Web Primer 89 How Should Reasoning be Done? Monotonic vs. Non-monotonic Rules Solution 2 R1: If birthday, then special discount R2’: If birthday is not known, then not special discount Solves the problem but: – The premise of rule R2' is not within the expressive power of predicate logic – We need a new kind of rule system to support non-monotonic rules

90. Chapter 4A Semantic Web Primer 90 Summary OWL is the proposed standard for Web ontologies OWL builds upon RDF and RDF Schema: – (XML-based) RDF syntax is used – Instances are defined using RDF descriptions – Most RDFS modeling primitives are used

91. Chapter 4A Semantic Web Primer 91 Summary (2) Formal semantics and reasoning support is provided through the mapping of OWL on logics – Predicate logic and description logics have been used for this purpose While OWL is sufficiently rich to be used in practice, extensions are in the making – They will provide further logical features, including rules

92. Interesting Aspects and Research Issues

93. Non-exhaustive List Efficient storage of RDF Efficient processing of SPARQL Efficient Inference Automatic extraction of text into RDF Ontology evolution and change Ontology mapping Inference and querying distributed RDF (limited resources and time) Successful RDF applications (data integration, life sciences) Description logics (and their relationship to RDF)