1. 1 © Copyright 2010 Dieter Fensel, Mick Kerrigan and Ioan Toma Artificial Intelligence Semantic Web and Services

2. 2 Where are we? #Title 1Introduction 2Propositional Logic 3Predicate Logic 4Reasoning 5Search Methods 6CommonKADS 7Problem-Solving Methods 8Planning 9Software Agents 10Rule Learning 11Inductive Logic Programming 12Formal Concept Analysis 13Neural Networks 14Semantic Web and Services

3. 3 Agenda Semantic Web - Data Motivation Development of the Web Internet Web 1.0 Web 2.0 Limitations of the current Web Technical Solution: URI, RDF, RDFS, OWL, SPARQL Illustration by Larger Examples: KIM Browser Plugin, Disco Hyperdata Browser Extensions: Linked Open Data Semantic Web – Processes Motivation Technical Solution: Semantic Web Services, WSMO, WSML, SEE, WSMX Illustration by Larger Examples: SWS Challenge, Virtual Travel Agency, WSMX at work Extensions: Mobile Services, Intelligent Cars, Intelligent Electricity Meters Summary References 3

4. 4 SEMANTIC WEB - DATA 4

5. 5 MOTIVATION 5

6. 6 DEVELOPMENT OF THE WEB

7. 7 Development of the Web 1.Internet 2.Web 1.0 3.Web 2.0

8. 8 INTERNET

9. 9 Internet “The Internet is a global system of interconnected computer networks that use the standard Internet Protocol Suite (TCP/IP) to serve billions of users worldwide. It is a network of networks that consists of millions of private and public, academic, business, and government networks of local to global scope that are linked by a broad array of electronic and optical networking technologies.” http://en.wikipedia.org/wiki/Internet

10. 10 19451995 Memex Conceived 1945 WWW Created 1989 Mosaic Created 1993 A Mathematical Theory of Communication 1948 Packet Switching Invented 1964 Silicon Chip 1958 First Vast Computer Network Envisioned 1962 ARPANET 1969 TCP/IP Created 1972 Internet Named and Goes TCP/IP 1984 Hypertext Invented 1965 Age of eCommerce Begins 1995 A brief summary of Internet evolution Source: http://www.isoc.org/internet/history2002_0918_Internet_History_and_Growth.ppt http://www.isoc.org/internet/history2002_0918_Internet_History_and_Growth.ppt

11. 11 WEB 1.0

12. 12 “The World Wide Web ("WWW" or simply the "Web") is a system of interlinked, hypertext documents that runs over the Internet. With a Web browser, a user views Web pages that may contain text, images, and other multimedia and navigates between them using hyperlinks”. http://en.wikipedia.org/wiki/World_Wide_Web Web 1.0

13. 13 Web 1.0 Netscape –Netscape is associated with the breakthrough of the Web. –Netscape had rapidly a large user community making attractive for others to present their information on the Web. Google –Google is the incarnation of Web 1.0 mega grows –Google indexed already in 2008 more than 1 trillion pages [*] –Google and other similar search engines turned out that a piece of information can be faster found again on the Web than in the own bookmark list [*] http://googleblog.blogspot.com/2008/07/we-knew-web-was-big.htmlhttp://googleblog.blogspot.com/2008/07/we-knew-web-was-big.html

14. 14 Web 1.0 principles The success of Web1.0 is based on three simple principles: 1.A simple and uniform addressing schema to indentify information chunks i.e. Uniform Resource Identifiers (URIs) 2.A simple and uniform representation formalism to structure information chunks allowing browsers to render them i.e. Hyper Text Markup Language (HTML) 3.A simple and uniform protocol to access information chunks i.e. Hyper Text Transfer Protocol (HTTP)

15. 15 1. Uniform Resource Identifiers (URIs) Uniform Resource Identifiers (URIs) are used to name/identify resources on the Web URIs are pointers to resources to which request methods can be applied to generate potentially different responses Resource can reside anywhere on the Internet Most popular form of a URI is the Uniform Resource Locator (URL)

16. 16 2. Hyper-Text Markup Language (HTML) Hyper-Text Markup Language: –A subset of Standardized General Markup Language (SGML) –Facilitates a hyper-media environment Documents use elements to “mark up” or identify sections of text for different purposes or display characteristics HTML markup consists of several types of entities, including: elements, attributes, data types and character references Markup elements are not seen by the user when page is displayed Documents are rendered by browsers

17. 17 3. Hyper-Text Transfer Protocol (HTTP) Protocol for client/server communication –The heart of the Web –Very simple request/response protocol Client sends request message, server replies with response message –Provide a way to publish and retrieve HTML pages –Stateless –Relies on URI naming mechanism

18. 18 WEB 2.0

19. 19 Web 2.0 “The term "Web 2.0" (2004–present) is commonly associated with web applications that facilitate interactive information sharing, interoperability, user-centered design, and collaboration on the World Wide Web” http://en.wikipedia.org/wiki/Web_2.0

20. 20 Web 2.0 Web 2.0 is a vaguely defined phrase referring to various topics such as social networking sites, wikis, communication tools, and folksonomies. Tim Berners-Lee is right that all these ideas are already underlying his original web ideas, however, there are differences in emphasis that may cause a qualitative change. With Web 1.0 technology a significant amount of software skills and investment in software was necessary to publish information. Web 2.0 technology changed this dramatically.

21. 21 Web 2.0 major breakthroughs The four major breakthroughs of Web 2.0 are: 1.Blurring the distinction between content consumers and content providers. 2.Moving from media for individuals towards media for communities. 3.Blurring the distinction between service consumers and service providers 4.Integrating human and machine computing in a new and innovative way

22. 22 1. Blurring the distinction between content consumers and content providers Wiki, Blogs, and Twiter turned the publication of text in mass phenomena, as flickr and youtube did for multimedia

23. 23 Social web sites such as del.icio.us, facebook, FOAF, linkedin, myspace and Xing allow communities of users to smoothly interweave their information and activities 2. Moving from a media for individuals towards a media for communities

24. 24 3. Blurring the distinction between service consumers and service providers Mashups allow web users to easy integrate services in their web site that were implemented by third parties

25. 25 4. Integrating human and machine computing in a new way Amazon Mechanical Turk - allows to access human services through a web service interface blurring the distinction between manually and automatically provided services

26. 26 LIMITATIONS OF THE CURRENT WEB

27. 27 The current Web has its limitations when it comes to: 1.finding relevant information 2.extracting relevant information 3.combining and reusing information Limitations of the current Web

28. 28 Finding information on the current Web is based on keyword search Keyword search has a limited recall and precision due to: –Synonyms: e.g. Searching information about “Cars” will ignore Web pages that contain the word “Automobiles” even though the information on these pages could be relevant –Homonyms: e.g. Searching information about “Jaguar” will bring up pages containing information about both “Jaguar” (the car brand) and “Jaguar” (the animal) even though the user is interested only in one of them Limitations of the current Web Finding relevant information

29. 29 Keyword search has a limited recall and precision due also to: –Spelling variants: e.g. “organize” in American English vs. “organise” in British English –Spelling mistakes –Multiple languages i.e. information about same topics in published on the Web on different languages (English, German, Italian,…) Current search engines provide no means to specify the relation between a resource and a term –e.g. sell / buy Limitations of the current Web Finding relevant information

30. 30 One-fit-all automatic solution for extracting information from Web pages is not possible due to different formats, different syntaxes Even from a single Web page is difficult to extract the relevant information Limitations of the current Web Extracting relevant information Which book is about the Web? What is the price of the book?

31. 31 Extracting information from current web sites can be done using wrappers Limitations of the current Web Extracting relevant information WEB HTML pages Layout Structured Data, Databases, XML Structure Wrapper extract annotate structure

32. 32 The actual extraction of information from web sites is specified using standards such as XSL Transformation (XSLT) [1] Extracted information can be stored as structured data in XML format or databases. However, using wrappers do not really scale because the actual extraction of information depends again on the web site format and layout Limitations of the current Web Extracting relevant information [1] http://www.w3.org/TR/xslt

33. 33 Tasks often require to combine data on the Web 1.Searching for the same information in different digital libraries 2.Information may come from different web sites and needs to be combined Limitations of the current Web Combining and reusing information

34. 34 Limitations of the current Web Combining and reusing information Example: I want travel from Innsbruck to Rome. 1.Searches for the same information in different digital libraries

35. 35 Limitations of the current Web Combining and reusing information Example: I want to travel from Innsbruck to Rome where I want to stay in a hotel and visit the city 2.Information may come from different web sites and needs to be combined

36. 36 How to improve the current Web? Increasing automatic linking among data Increasing recall and precision in search Increasing automation in data integration Increasing automation in the service life cycle Adding semantics to data and services is the solution!

37. 37 TECHNICAL SOLUTIONS 37

38. 38 Uniform Resource Identifier Uniform Resource Identifiers (URIs) are used to identify resources, not just things that exists on the Web, e.g. Dieter Fensel, University of Innsbruck Taken from http://www.w3.org/TR/webarch/

39. 39 Resource Description Framework (RDF) The Resource Description Framework (RDF) provides a domain independent data model Resource (identified by URIs) –Correspond to nodes in a graph –E.g.: http://www.w3.org/ http://example.org/#john http://www.w3.org/1999/02/22-rdf-syntax-ns#Property Properties (identified by URIs) –Correspond to labels of edges in a graph –Binary relation between two resources –E.g.: http://www.example.org/#hasName http://www.w3.org/1999/02/22-rdf-syntax-ns#type Literals –Concrete data values –E.g.: "John Smith", "1", "2006-03-07 "

40. 40 Resource Description Framework (RDF) – Triple Data Model Triple data model: –Subject: Resource or blank node –Predicate: Property –Object: Resource, literal or blank node Example: Statement (or triple) as a logical formula P(x, y), where the binary predicate P relates the object x to the object y. RDF offers only binary predicates (properties)

41. 41 Resource Description Framework (RDF) – Graph Model The triple data model can be represented as a graph Such graph is called in the Artificial Intelligence community a semantic net Labeled, directed graphs –Nodes: resources, literals –Labels: properties –Edges: statements ex:john ex:bill ex:father-of ex:tom ex:father-of

42. 42 RDF Schema (RDFS) RDF Schema (RDFS) is a language for capturing the semantics of a domain, for example: –In RDF: –What is a “ #Student ”? RDFS is a language for defining RDF types: –Define classes: “ #Student is a class” –Relationships between classes: “ #Student is a sub-class of #Person ” –Properties of classes: “ #Person has a property hasName ”

43. 43 RDF Schema (RDFS) Classes: Class hierarchies: Properties: Property hierarchies: Associating properties with classes (a): –“The property #hasName only applies to #Person ” Associating properties with classes (b): –“The type of the property #hasName is #xsd:string ” 43

44. 44 RDF Schema (RDFS) - Example 44

45. 45 Web Ontology Language (OWL) RDFS has a number of Limitations: –Only binary relations –Characteristics of Properties, e.g. inverse, transitive, symmetric –Local range restrictions, e.g. for class Person, the property hasName has range xsd:string –Complex concept descriptions, e.g. Person is defined by Man and Woman –Cardinality restrictions, e.g. a Person may have at most 1 name –Disjointness axioms, e.g. nobody can be both a Man and a Woman The Web Ontology Language (OWL) provides an ontology language, that is a more expressive Vocabulary Definition Language for use with RDF –Class membership –Equivalance of classes –Consistency –Classification

46. 46 OWL OWL is layered into languages of different expressiveness –OWL Lite: Classification Hierarchies, Simple Constraints –OWL DL: Maximal expressiveness while maintaining tractability –OWL Full: Very high expressiveness, loses tractability, all syntactic freedom of RDF More expressive means harder to reason with Different Syntaxes: –RDF/XML (Recommended for Serialization) –N3 (Recommended for Human readable Fragments) –Abstract Syntax (Clear Human Readable Syntax) 46 lite DL Full

47. 47 OWL – Example: The Wine Ontology An Ontology describing wine domain One of the most widely used examples for OWL and referenced by W3C. There is also a wine agent associated to this ontology that performs OWL queries using a web-based ontological mark-up language. That is, by combining a logical reasoner with an OWL ontology. The agent's operation can be described in three parts: consulting the ontology, performing queries and outputting results. Available here: http://www.w3.org/TR/owl-guide/

48. 48 OWL – Example: The Wine Ontology Schema [http://mysite.verizon.net/jflynn12/VisioOWL/VisioOWL.htm]

49. 49 SPARQL – Querying RDF SPARQL –RDF Query language –Based on RDQL –Uses SQL-like syntax Example: PREFIX uni: SELECT ?name FROM WHERE { ?s uni:name ?name. ?s rdf:type uni:lecturer }

50. 50 SPARQL Queries PREFIX uni: SELECT ?name FROM WHERE { ?s uni:name ?name. ?s rdf:type uni:lecturer } PREFIX –Prefix mechanism for abbreviating URIs SELECT –Identifies the variables to be returned in the query answer –SELECT DISTINCT –SELECT REDUCED FROM –Name of the graph to be queried –FROM NAMED WHERE –Query pattern as a list of triple patterns LIMIT OFFSET ORDER BY

51. 51 SPARQL Example Query 1 “Return the full names of all people in the graph” PREFIX vCard: SELECT ?fullName WHERE {?x vCard:FN ?fullName} result: fullName ================= "John Smith" "Mary Smith" @prefix ex:. @prefix vcard:. ex:john vcard:FN "John Smith" ; vcard:N [ vcard:Given "John" ; vcard:Family "Smith" ] ; ex:hasAge 32 ; ex:marriedTo :mary. ex:mary vcard:FN "Mary Smith" ; vcard:N [ vcard:Given "Mary" ; vcard:Family "Smith" ] ; ex:hasAge 29.

52. 52 SPARQL Example Query 2 “Return the relation between John and Mary” PREFIX ex: SELECT ?p WHERE {ex:john ?p ex:mary} result: p ================= @prefix ex:. @prefix vcard:. ex:john vcard:FN "John Smith" ; vcard:N [ vcard:Given "John" ; vcard:Family "Smith" ] ; ex:hasAge 32 ; ex:marriedTo :mary. ex:mary vcard:FN "Mary Smith" ; vcard:N [ vcard:Given "Mary" ; vcard:Family "Smith" ] ; ex:hasAge 29.

53. 53 SPARQL Example Query 3 “Return the spouse of a person by the name of John Smith” PREFIX vCard: PREFIX ex: SELECT ?y WHERE {?x vCard:FN "John Smith". ?x ex:marriedTo ?y} result: y ================= @prefix ex:. @prefix vcard:. ex:john vcard:FN "John Smith" ; vcard:N [ vcard:Given "John" ; vcard:Family "Smith" ] ; ex:hasAge 32 ; ex:marriedTo :mary. ex:mary vcard:FN "Mary Smith" ; vcard:N [ vcard:Given "Mary" ; vcard:Family "Smith" ] ; ex:hasAge 29.

54. 54 ILLUSTRATION BY LARGER EXAMPLES 54

55. 55 Annotated Content KIM Browser Plugin Web content is annotated using ontologies Content can be searched and browsed intelligently Select one or more concepts from the ontology… … send the currently loaded web page to the Annotation Server Illustration 1 – KIM Browser Plugin 55

56. 56 Dereferencable URI Disco Hyperdata Browser navigating the Semantic Web as an unbound set of data sources Illustration 2 – Disco Hyperdata Browser 56

57. 57 EXTENSIONS 57

58. 58 Extensions: Linked Open Data Linked Data is a method for exposing and sharing connected data via dereferenceable URI’s on the Web –Use URIs to identify things that you expose to the Web as resources –Use HTTP URIs so that people can locate and look up (dereference) these things –Provide useful information about the resource when its URI is dereferenced –Include links to other, related URIs in the exposed data as a means of improving information discovery on the Web Linked Open Data is an initiative to interlink open data sources –Open: Publicly available data sets that are accessible to everyone –Interlinked: Datasets have references to one another allowing them to be used together 58

59. 59 Extensions: Linked Open Data 59

60. 60 Extensions: Linked Open Data - FOAF Friend Of A Friend (FOAF) provides a way to create machine-readable pages about: –People –The links between them –The things they do and create Anyone can publish a FOAF file on the web about themselves and this data becomes part of the Web of Data FOAF is connected to many other data sets, including –Data sets describing music and musicians (Audio Scrobbler, MusicBrainz) –Data sets describing photographs and who took them (Flickr) –Data sets describing places and their relationship (GeoNames) Dieter Fensel Dieter Fensel 60

61. 61 Extensions: Linked Open Data - GeoNames The GeoNames Ontology makes it possible to add geospatial semantic information to the Web of Data We can utilize GeoNames location within the FOAF profile GeoNames is also linked to more datasets –US Census Data –Movie Database (Linked MDB) –Extracted data from Wikipedia (DBpedia) Dieter Fensel Dieter Fensel 61

62. 62 Extensions: Linked Open Data - DBpedia DBpedia is a community effort to extract structured information from Wikipedia and to make this information available on the Web As our FOAF profile has been linked to GeoNames, and GeoNames is linked to DBpedia, we can ask some interesting queries over the Web of Data –What is the population of the city in which Dieter Fensel lives? => 117916 people –At which elevation does Dieter Fensel live? => 574m –Who is the mayor of the city in which Dieter Fensel lives => Hilde Zach 62

63. 63 SEMANTIC WEB - PROCESSES 63

64. 64 MOTIVATION 64

65. 65 Motivation http://www.sti-innsbruck.at/dip-movie

66. 66 Motivation The Web is moving from static data to dynamic functionality –Web services: a piece of software available over the Internet, using standardized XML messaging systems –Mashups: The compounding of two or more pieces of web functionality to create powerful web applications

67. 67 Motivation

68. 68 Limitations of the current Web Processes Web services and mashups are limited by their syntactic nature As the amount of services on the Web increases it will be harder to find Web services in order to use them in mashups The current amount of human effort required to build applications is not sustainable at a Web scale

69. 69 What is needed? Formal, machine processable descriptions of processes on the Web that allows easy integration, configuration and reuse Semantic support for finding, composing and executing these processes and all the other related tasks Solution: Combine Semantics and Web processes/services that enables the automation of many of the currently human intensive tasks around Web processes/services

70. 70 TECHNICAL SOLUTIONS 70

71. 71 Semantic Web Services Brings the benefits of Semantics to the executable part of the Web –Ontologies as data model –Unambiguous definition of service functionality and external interface Reduce human effort in integrating services in SOA –Many tasks in the process of using Web services can be automated Improve dynamism –New services available for use as they appear –Service Producers and Consumers don’t need to know of each others existence Improve stability –Service interfaces are not tightly integrated so even less impact from changes –Services can be easily replaced if they are no longer available –Failover possibilities are limited only by the number of available services 71

72. 72 Semantic Web Services Semantic Web Services are a layer on top of existing Web service technologies and do not aim to replace them Provide a formal description of services, while still being compliant with existing and emerging technologies Distinguish between a Web service (computational entity) and a service (value provided by invocation) Make Web services easier to: –Find –Compare –Compose –Invoke 72

73. 73 Conceptual Model for SWS Formal Language for WSMO Ontology & Rule Language for the Semantic Web Technical Overview 73 Execution Environment For SWS

74. 74 Strict Decoupling of Modeling Elements Centrality of Mediation Ontological Role Separation Description versus Implementation WSMO Ontology-Based Web Service versus Service WSMO – Design Principles 74

75. 75 WSMO – Conceptual Model Objectives that a client wants to achieve by using Web Services Formally specified terminology used by all other components Semantic description of Web Services Capability (functional)‏ Interfaces (usage) Connectors between components with mediation facilities for handling heterogeneities 75

76. 76 WSML – Language Family WSML - Core WSML - DL WSML - Full WSML - Flight WSML - Rule f with without 76 Expressivity

77. 77 Semantic Execution Environment 77 Semantic Execution Environment Discovery Ranking Selection Composition Data Mediation Process Mediation Process Execution Lifting & Lowering base broker vertical Monitoring Reasoning Storage

78. 78 Semantic Execution Environment - WSMX 78

79. 79 ILLUSTRATION BY LARGER EXAMPLES 79

80. 80 Blue company has discovered Moon company on the Web Blue company wishes to communicate with Moon company Broker required to resolve data and process interoperability issues Purchase Order Confirmation id cid openOrder addItem* closeOrder Blue Company can only send POs and receive PO Confirmations Allows the opening of a PO, the specification of the items to be purchased and the closing of the PO Receives a customer id and returns a full customer description Illustration 1: SWS Challenge 80

81. 81 Rail Services Flight Services Hotel Services Car Hire Services Illustration 2: Virtual Travel Agency 81

82. 82 Illustration 3: WSMX At Work 82

83. 83 Illustration 3: WSMX At Work Request to discover Web services. 83

84. 84 Illustration 3: WSMX At Work Goal expressed in WSML is sent to WSMX System Interface 84

85. 85 Illustration 3: WSMX At Work Com. M. implements the interface to receive WSML goals 85

86. 86 Illustration 3: WSMX At Work Com. M. informs Core that Goal has been received 86

87. 87 Illustration 3: WSMX At Work Chor. wrapper picks up event for Chor. component 87

88. 88 Illustration 3: WSMX At Work New choreography Instance is created 88

89. 89 Illustration 3: WSMX At Work Core is notified that choreography instance has been created. 89

90. 90 Illustration 3: WSMX At Work WSML goal is parsed to internal format. 90

91. 91 Illustration 3: WSMX At Work Discovery is invoked for parsed goal. 91

92. 92 Illustration 3: WSMX At Work Discovery may requires ontology mediation. 92

93. 93 Illustration 3: WSMX At Work After data mediation, Discovery iterates, if needed through last steps until result set is finished. 93

94. 94 Illustration 3: WSMX At Work Selection is invoked to relax result set to finally one service. 94

95. 95 Illustration 3: WSMX At Work Choreography instance for goal requester is checked for next steps. 95

96. 96 Illustration 3: WSMX At Work Result is returned to Com. Man. to be forwarded to the service requester. 96

97. 97 Illustration 3: WSMX At Work Set of Web Service descriptions expressed in WSML sent to adapter. 97

98. 98 Illustration 3: WSMX At Work Set of Web Service descriptions expressed in requester’s own format returned to goal requester. 98

99. 99 EXTENSIONS 99

100. 100 Extensions: Mobile Services

101. 101 Extensions: Mobile Services Extending the mobile and sensors networks with Semantic technologies, Semantic Web will enable: –Interoperability at the level of sensors data and protocols –More precise search for mobile capabilities and sensors with desired capability From: http://www.opengeospatial.org/projects/groups/sensorweb

102. 102 Extensions: Intelligent Cars

103. 103 Extensions: Intelligent Cars A key aspect of future cars will be intelligence –Real-time data collection –Car-to-X communication –Interaction Systems –Driver Assistance Semantic Technologies can be used to add this intelligence through: –Machine awareness of context (Spatio-temporal concerns, mood, etc..) –Management of large volumes of real-time data –Enabling interoperability between systems From: http://www.nff.tu-bs.de/index.php?id=464&L=1

104. 104 Extensions: A Smarter Electricity Grid

105. 105 Extensions: A Smarter Electricity Grid Managing increasingly dispersed energy resources is crucial to ensure efficient electricity consumption, for example –Wind farms –Solar panels Grid operators cannot be sure at any given time: –Which electricity generators are connected –If they are operational or not –How much power they are outputting Semantic technologies can be used to enable “self-describing networks” –Each member of the network autonomously publishes semantic data about itself –Enables more efficient automated grid management technologies Ultimately semantic technologies can provide a “Smart Electricity Grid”

106. 106 SUMMARY 106

107. 107 Summary The Semantic Web provides a mechanism for –Representing knowledge on the Web –Annotating data on the Web –Annotating services on the Web Everything is identified by a URI RDF to assert relations between resources RDFS and OWL to make statements about types SPARQL to query RDF data WSMO as a conceptual model WSML for describing services at different levels of expressivity WSMX as a Semantic Execution Environment for bringing requesters and providers together 107

108. 108 REFERENCES 108

109. 109 References Mandatory reading: –T. Berners-Lee, J. Hendler, O. Lassila. The Semantic Web, Scientific American, 2001. –Dieter Fensel, Holger Lausen, Axel Polleres, Jos de Bruijn, Michael Stollberg, Dumitru Roman, John Domingue, Enabling Semantic Web Services: The Web Service Modeling Ontology, Springer-Verlag, 2007 Further reading: –Dieter Fensel, Mick Kerrigan, Michal Zaremba (Eds.), Implementing Semantic Web Services: The SESA Framework. Springer-Verlag, 2008. – Jos de Bruijn, Dieter Fensel, Mick Kerrigan, Uwe Keller, Holger Lausen, and James Scicluna: Modeling Semantic Web Services, Springer-Verlag, 2008 –D. Fensel. Ontologies: A Silver Bullet for Knowledge Management and Electronic Commerce, 2nd Edition, Springer 2003. –G. Antoniou and F. van Harmelen. A Semantic Web Primer, (2nd edition), The MIT Press 2008. –H. Stuckenschmidt and F. van Harmelen. Information Sharing on the Semantic Web, Springer 2004. –T. Berners-Lee. Weaving the Web, HarperCollins 2000 –T.R. Gruber, Toward principles for the design of ontologies used or knowledge sharing?, Int. J. Hum.-Comput. Stud., vol. 43, no. 5-6,1995

110. 110 References –David Martin, et al., OWL-S: Semantic Markup for Web Services, W3C Member Submission 22 November 2004, http://www.w3.org/Submission/OWL-S. –Joel Farrell and Holger Lausen, Semantic Annotations for WSDL and XML Schema, W3C Recommendation 28 August 2007, http://www.w3.org/TR/sawsdlhttp://www.w3.org/TR/sawsdl –Rama Akkiraju et al., Web Service Semantics - WSDL-S, W3C Member Submission 7 November 2005, http://www.w3.org/Submission/WSDL-Shttp://www.w3.org/Submission/WSDL-S –Steve Battle et al., Semantic Web Services Framework (SWSF), W3C Member Submission 9 September 2005, http://www.w3.org/Submission/SWSFhttp://www.w3.org/Submission/SWSF –Semantic Web Primer: http://www.ics.forth.gr/isl/swprimer/http://www.ics.forth.gr/isl/swprimer/ –RDF Primer: http://www.w3.org/TR/REC-rdf-syntax/http://www.w3.org/TR/REC-rdf-syntax/ –RDF Schema: http://www.w3.org/TR/rdf-schema/http://www.w3.org/TR/rdf-schema/ –OWL Guide: http://www.w3.org/TR/owl-guide/http://www.w3.org/TR/owl-guide/ –RDF SPARQL Protocol: http://www.w3.org/TR/rdf-sparql-protocol/http://www.w3.org/TR/rdf-sparql-protocol/ –Linked Open Data: http://linkeddata.org/http://linkeddata.org/ –Linked Open Data Tutorial: http://www4.wiwiss.fu-berlin.de/bizer/pub/LinkedDataTutorial/http://www4.wiwiss.fu-berlin.de/bizer/pub/LinkedDataTutorial/ –WSMO: http://www.wsmo.org/TR/d2/http://www.wsmo.org/TR/d2/ –WSML: http://www.wsmo.org/TR/d16/d16.1/http://www.wsmo.org/TR/d16/d16.1/ –WSMO/WSML Tutorials: http://wiki.sti2.at/index.php?title=WSMT_Tutorialshttp://wiki.sti2.at/index.php?title=WSMT_Tutorials

111. 111 References Wikipedia links: –URI: http://en.wikipedia.org/wiki/URIhttp://en.wikipedia.org/wiki/URI –RDF; http://en.wikipedia.org/wiki/Resource_Description_Frameworkhttp://en.wikipedia.org/wiki/Resource_Description_Framework –RDFS: http://en.wikipedia.org/wiki/RDFShttp://en.wikipedia.org/wiki/RDFS –SPARQL: http://en.wikipedia.org/wiki/SPARQLhttp://en.wikipedia.org/wiki/SPARQL –WSMO: http://en.wikipedia.org/wiki/WSMOhttp://en.wikipedia.org/wiki/WSMO –WSML: http://en.wikipedia.org/wiki/Web_Services_Modeling_Languagehttp://en.wikipedia.org/wiki/Web_Services_Modeling_Language

112. 112 http://www.sti-innsbruck.at/results/movies/serviceweb30-the-future-internet/

113. 113 Questions?