1. Dr. Alexandra I. Cristea http://www.dcs.warwick.ac.uk/~acristea/ Semantic Web

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3. Requirements of the WWW The internet - already there HTML programmers Search engines Core weight of interest 3

4. Why do we need the Semantic Web? I have a dream for the Web [in which computers] become capable of analyzing all the data on the Web –– the content, links, and transactions between people and computers....the day-to-day mechanisms of trade, bureaucracy and our daily lives will be handled by machines talking to machines. Tim Berners-Lee (1999) Weaving the Web 4

5. 5 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:

6. 6 Where we (still) are Today: the Syntactic Web [Hendler & Miller 02]

7. 7 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]

8. 8 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

9. 9 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

10. 10 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…

11. 11 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

12. 12 What information can a machine see…                   

13. 13 Solution: XML markup with “meaningful” tags?            

14. 14 But What About…           

15. 15 Machine sees…             

16. A more current scenario What are you doing on Burns night? –Google “burns” –Wikipedia articles on Robert Burns –Amazon listing of books by Burns –Google Maps to look at birthplace of Burns 16

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20. 20 Google Maps

21. Combining Information 21

22. Combining one source with a service from another 22

23. Web APIs A large and growing number of web data sources provide program-accessible interfaces (APIs). The web site http://www.programmableweb.com currently (October 2015) lists over 14123. Most popular Web APIs are: 23

24. Limitations of Web APIs The interfaces are non-uniform - REST, RPC (e.g., SOAP) and hybrid The results are returned in variety of formats - XML, JSON, Atom The data schemas tend to be provider- specific Militates against the development of portable, generic methods of accessing and using data. 24

25. 25 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…

26. The semantic web Invented by Tim Berners-Lee and others. W3C driving organisation. –Web of machine-readable data What are the main aims of the SW? –Automated query-answering –Automated use of the data (reasoning, planning,acting, etc) 26

27. WWW v Semantic Web WWW is a web of documents SW is a web of data WWW documents are human readable SW data is machine readable (in theory at least) Shared AAA principle: Anyone can say Anything, Anywhere. 27

28. Why the Semantic Web? I don’t think [the Semantic Web is] a very good name but we’re stuck with it now. The word semantics is used by different groups to mean different things...I think we could have called it the Data Web....it connects all applications together or gives [people] access to data across the company... Tim Berners-Lee (2007), Interview in Business Week 28

29. Why the Semantic Web? Syntax / semantics distinction: long history in philosophy of language, linguistics, formal logic Syntax concerned with arrangement of symbols Semantics concerned with the relation between symbols strings and the world: what things actually mean. 29

30. What can the Semantic Web actually do? Query answering: IBM’s Watson: beats human competitors at JeopardyIBM’s Watson but specifically trained for this task (including looking at decade’s worth of past Jeopardy answers) sort of cheating (reaction times means it always gets first go!) 30

31. What can the Semantic Web actually do? Query answering: Wolfram-alpha: does complex query- answering and solves mathematical problemsWolfram-alpha but hand-curated database - not the Semantic Web hugely labour-intensive to develop and cannot take advantage of new knowledge 31

32. What can the Semantic Web actually do? Query answering: Other systems: –considerable progress –current state-of-the-art is extremely useful but the general case is hard! 32

33. What can the Semantic Web actually do? Automated use of data: works well in constrained circumstances: –for example: Google maps can automatically combine information about maps, speed limits, current road usage, etc., to get estimates of journey time very hard in unconstrained circumstances: –classic SW example of an automated travel agent still far from achievable 33

34. What are the requirements of the Semantic Web? Large numbers of users to make their data: –available –in an appropriate machine-readable format This is happening now: open government data (esp. in UK and US) and many other organisations and individuals: https://www.data.gov.uk/ https://www.data.gov/ >> find more open data repositories as homework! https://www.data.gov.uk/https://www.data.gov/ Good query-answering systems The ability to automatically interpret and use data 34

35. 35 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

36. Ontology Languages for the Semantic Web

37. What is an ontology? Originally: a definitive account of what exists (derived from metaphysics). Therefore, we can create a single ontology that describes the world – maybe dividing into smaller sub-ontologies as necessary. But this is completely misconceived! 37

38. Same world-view? Check as a homework other definitions of the word ‘ontologies’ via Google. Hence ‘Ontology merging’ a hot research area! 38

39. Ontologies in the SW A way of encoding domain knowledge, linking the knowledge, which allows for reasoning with the data Dictionary/ Vocabulary   Taxonomy   Ontology Ontologies allow for data integration and inference, for automated query-answering and automated use of data 39

40. Why Semantic Web ontologies? data integration 40

41. data integration inference 41 Why Semantic Web ontologies? William Burnes is the father of Robert Burns. … Father is a subclass of parent. … William Burnes is the parent of Robert Burns.

42. 42 Why Semantic Web ontologies? data integration Inference Automated query-answering Automated use of data

43. http://semanticweb.org/ 43 LanguageSwoogle hitsRevised Dublin CoreRDF1,364,33728 October 2006 FOAFOWL DL1,194,87127 July 2005 TrackBackRDF502,401 MetaVocabRDF441,79016 February 2002 Basic Geo VocabularyRDF Schema248,1301 February 2006 BIORDF220,2285 March 2004 RSS 1.0RDF Schema201,7866 December 2000 VCard RDFRDF181,96222 February 2001 Creative Commons metadataRDF Schema112,216 WOTOWL DL97,29223 February 2004 SIOCOWL DL42,91111 April 2008 GoodRelationsOWL DL5,0001 October 2011 DOAPRDF Schema1,4425 November 2005 Programmes OntologyOWL 29437 September 2009 Music OntologyOWL 264614 February 2010 OpenGUIDRDF Schema124 September 2008 Provenance VocabularyOWL DL125 August 2009 Pedagogical diagnosisOWL DL11 April 2012 DILIGENT Argumentation OntologyOWL 2113 September 2006 Example Ontologies

44. 44 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

45. 45 Example Ontology

46. 46 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

47. 47 Ontology Design and Deployment Given key role of ontologies in the Semantic Web, it is 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)

48. 48 The Semantic Web Shared ontologies help to exchange data and meaning between web-based services (Image by Jim Hendler)

49. 49 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

50. 50 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)

51. 51 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)

52. 52 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:

53. 53 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)

54. Protégé 54

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56. 56 (In)famous “Layer Cake”  Data Exchange  Semantics+reasoning  Relational Data ? ? ??? Relationship between layers is not clear OWL DL extends “DL subset” of RDF

57. 57

58. Linked Data 58

59. Semantic web: Linked Data Isn’t just about putting data on the Web It’s about making links Web of Hypertext -> Web of Data 59

60. Linked Data: The four rules 1.Use URIs as names for things. 2.Use HTTP URIs so that people can look up those names. 3.When someone looks up a URI, provide useful information, using the standards (RDF*, SPARQL). 4.Include links to other URIs, so that they can discover more things. 60

61. Why HTTP URIs? Globally unique names –can be created in a decentralised fashion by domain name owners; –no central naming authority is required. Not just a name, but a means of accessing information describing the identified entity. (URL) 61

62. URIs These URIs point to web documents - or in the terminology of WebArch, information resources. –by definition, all its essential characteristics can be conveyed in a message Web clients request a representation of a resource One and the same resource might have different representations; e.g., text in English, Greek, Chinese, etc. 62 Homepage of the Department of Computer Science http://www.dcs.warwick.ac.uk/ Homepage of the Department of Computer Science http://www.dcs.warwick.ac.uk/ Homepage of Alexandra Cristea http://www2.warwick.ac.uk/fac/sci/dcs/people/Alexandra_Cristea Homepage of Alexandra Cristea http://www2.warwick.ac.uk/fac/sci/dcs/people/Alexandra_Cristea

63. Content Negotiation HTTP clients send HTTP headers with each request to indicate what kinds of documents they prefer. Client can say prefers language X over Y. Or prefers RDF over HTML. Servers inspect headers and select an appropriate response. 63 GET /fac/sci/dcs/people/Alexandra_Cristea HTTP/1.1 Host: www2.warwick.ac.uk Accept: text/html, application/xhtml+xml Accept Language: en, gr, cn Header of GET Requests HTTP/1.1 200 OK Content -Type: text/html Content-Language: en Servers Response

64. URIs for Things We need mechanisms to ensure that when URIs are dereferenced, –real-world objects are not confused with documents that describe them, and –humans as well as machines can retrieve appropriate representations. 64

65. RDF for Linked Data RDF is standardly used for Linked Data. Advantages include: –Easy to insert RDF links between data from different sources. –Information from different sources can be combined by graph merging. –Information using different schemas can be expressed in a single graph, i.e., by mixing different vocabularies. –Data can be tightly or loosely structured. Features of RDF that are avoided: –Reification (hard to query with SPARQL) –Collections and containers (ditto). Use multiple triples with same predicate instead. –Blank nodes: makes merging less effective. 65

66. Kinds of Links Relationship Links –related things in other data sources. ≈ hyperlinks in a web document. –e.g. foaf:based_neardbpedia:Edinburgh Identity Links –URI aliases of other data sources for the same (real- world/abstract) object. Vocabulary Links –definitions of vocabulary terms used to represent the data. 66

67. Identity Links different URIs may refer to same real-world object. –Standard for equivalence: http://www.w3.org/2002/07/owl#sameAs. Motivations for this approach: –Different aliases can be dereferenced to different description of same resource (AAA principle).AAA principle –Support provenance : trace back to publisher of URI. –canonic > centralised naming authority > barrier to spread web of data. Potential problems: –Identity may be context dependent –Facts vs. opinions 67

68. 68

69. Is Your Data 5- ★ ? 69

70. Reflecting on Linked Data Structured data –available on web (i.e. open) in many formats: –CSV, Excel, HTML Microdata(e.g. http://schema.org/), web APIs, PDF tables (shudder),... Advantages of Linked Data: –A unifying data model (RDF) –A standardised data access mechanism (HTTP) –Hyperlink-based data discovery: links connect all Linked Data into a single global data space and enable Linked Data applications to discover new data sources at run-time. –Self-descriptive data: vocabulary definitions are recoverable like other data, and vocabulary terms can be linked to one another. 70

71. Reflecting on Linked Data Linked data adopts perspective of data integration. –Not (necessarily) interested in reasoning aspect of Semantic Web. http://blog.paulwalk.net/2009/11/11/linked-open- semantic/:http://blog.paulwalk.net/2009/11/11/linked-open- semantic/: –Data can be open, while not being linked. –Data can be linked, while not being open. –Data which is both open and linked is increasingly viable. –The Semantic Web can only function with data which is both open and linked. 71

72. Web of Data (Linked Data) 72

73. Summary Linked Data Linked Data principles –Naming things with URIs –Making URIs dereferenceable –Providing useful RDF information –Including links to other things 73

74. 74 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

75. Finding out more on SW Course website and recommended reading Do your homeworks! There is lots of relevant literature online – try to explore it Also a lot of informal discussion on Twitter, newsgroups, YouTube, etc. 75