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

© Copyright 2010 Dieter Fensel, Mick Kerrigan and Ioan Toma 1 2

Agenda

• Semantic Web - Data • Motivation • Development of the Web • • 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 SEMANTIC WEB - DATA • Illustration by Larger Examples: SWS Challenge, Virtual Travel Agency, WSMX at work • Extensions: Mobile Services, Intelligent Cars, Intelligent Electricity Meters • Summary • References

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1 MOTIVATION DEVELOPMENT OF THE WEB

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Development of the Web

1. Internet 2. Web 1.0 3. Web 2.0

INTERNET

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2 Internet A brief summary of Internet evolution

Age of eCommerce Mosaic Begins WWW • “The Internet is a global system of interconnected Internet Created 1995 Created 1993 Named 1989 computer networks that use the standard Internet and Goes Protocol Suite (TCP/IP) to serve billions of users TCP/IP TCP/IP Created 1984 ARPANET 1972 worldwide. It is a network of networks that consists of 1969 millions of private and public, academic, business, and Invented Packet 1965 government networks of local to global scope that are Switching First Vast Invented linked by a broad array of electronic and optical Computer 1964 Network networking technologies.” Silicon Envisioned A Chip 1962 Mathematical 1958 Theory of Memex Communication http://en.wikipedia.org/wiki/Internet Conceived 1948 1945

1945 1995 Source: http://www.isoc.org/internet/history2002_0918_Internet_History_and_Growth.ppt 9 10

Web 1.0

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

WEB 1.0

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3 Web 1.0 Web 1.0 principles

• Netscape • The success of Web1.0 is based on three simple – Netscape is associated with the breakthrough of the Web. principles: – Netscape had rapidly a large user community making attractive 1. A simple and uniform addressing schema to indentify for others to present their information on the Web. information chunks i.e. Uniform Resource Identifiers (URIs) 2. A simple and uniform representation formalism to structure • Google information chunks allowing browsers to render them i.e. Hyper – Google is the incarnation of Web 1.0 mega grows Text Markup Language (HTML) – Google indexed already in 2008 more than 1 trillion pages [*] 3. A simple and uniform protocol to access information chunks i.e. – Google and other similar search engines turned out that a piece Hyper Text Transfer Protocol (HTTP) 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.html

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1. Uniform Resource Identifiers (URIs) 2. Hyper-Text Markup Language (HTML)

• Uniform Resource Identifiers (URIs) are used to name/ • Hyper-Text Markup Language: identify resources on the Web – A subset of Standardized General Markup Language (SGML) – Facilitates a hyper-media environment • URIs are pointers to resources to which request methods can be applied to generate potentially different • Documents use elements to “mark up” or identify sections of text for different purposes or display responses characteristics • Resource can reside anywhere on the Internet • HTML markup consists of several types of entities, • Most popular form of a URI is the Uniform Resource including: elements, attributes, data types and character Locator (URL) references • Markup elements are not seen by the user when page is displayed • Documents are rendered by browsers

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4 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 WEB 2.0

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Web 2.0 Web 2.0

• “The term "Web 2.0" (2004–present) is commonly • Web 2.0 is a vaguely defined phrase referring to various associated with web applications that facilitate interactive topics such as social networking sites, wikis, information sharing, interoperability, user-centered design, communication tools, and . and collaboration on the World Wide Web” • Tim Berners-Lee is right that all these ideas are already underlying his original web ideas, however, there are http://en.wikipedia.org/wiki/Web_2.0 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.

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5 1. Blurring the distinction between content Web 2.0 major breakthroughs consumers and content providers

Wiki, Blogs, and Twiter turned the publication of text in mass phenomena, as flickr and youtube did for multimedia • 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

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2. Moving from a media for individuals 3. Blurring the distinction between service towards a media for communities consumers and service providers

Social web sites such as del.icio.us, facebook, FOAF, Mashups allow web users to easy integrate services in their linkedin, myspace and Xing allow communities of users to web site that were implemented by third parties smoothly interweave their information and activities

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6 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

LIMITATIONS OF THE CURRENT WEB

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Limitations of the current Web Limitations of the current Web Finding relevant information

• Finding information on the current Web is based on • The current Web has its limitations when it keyword search comes to: • Keyword search has a limited recall and precision due to: 1. finding relevant information – Synonyms: • e.g. Searching information about “Cars” will ignore Web pages that contain 2. extracting relevant information the word “Automobiles” even though the information on these pages could 3. combining and reusing information 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

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7 Limitations of the current Web Limitations of the current Web Finding relevant information Extracting relevant information

• One-fit-all automatic solution for extracting information from Web pages is • Keyword search has a limited recall and precision due not possible due to different formats, different syntaxes also to: • Even from a single Web page is difficult to extract the relevant information – 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,…) Which book is about the Web? • Current search engines provide no means to specify the relation between a resource and a term What is the price – e.g. sell / buy of the book?

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Limitations of the current Web Limitations of the current Web Extracting relevant information Extracting relevant information

• Extracting information from current web sites can be • The actual extraction of information from web sites is done using wrappers specified using standards such as XSL Transformation (XSLT) [1] • Extracted information can be stored as structured data in XML format or . Wrapper • However, using wrappers do not really scale because WEB Structured Data, Databases, the actual extraction of information depends again on the HTML pages extract XML web site format and layout Layout annotate Structure structure

[1] http://www.w3.org/TR/xslt

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8 Limitations of the current Web Limitations of the current Web Combining and reusing information Combining and reusing information

1. Searches for the same information in different digital • Tasks often require to combine data on the libraries Example: I want travel from Innsbruck to Rome. Web 1. Searching for the same information in different digital libraries 2. Information may come from different web sites and needs to be combined

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Limitations of the current Web How to improve the current Web? Combining and reusing information

2. Information may come from different web sites and needs to be combined Example: I want to travel from Innsbruck to Rome where I want to stay in a • Increasing automatic linking among data hotel and visit the city • Increasing recall and precision in search • Increasing automation in data integration • Increasing automation in the service life cycle

• Adding to data and services is the solution!

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9 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

TECHNICAL SOLUTIONS

Taken from http://www.w3.org/TR/webarch/

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Resource Description Framework (RDF) – Resource Description Framework (RDF) Triple Data Model

• The Resource Description Framework (RDF) provides a domain • Triple data model: independent data model

• Resource (identified by URIs) – Subject: Resource or blank node – Correspond to nodes in a graph – Predicate: Property – E.g.: – Object: Resource, literal or blank node http://www.w3.org/ http://example.org/#john http://www.w3.org/1999/02/22-rdf-syntax-ns#Property • Example: • Properties (identified by URIs) – Correspond to labels of edges in a graph – Binary relation between two resources – E.g.: • Statement (or triple) as a logical formula P(x, y), where the binary http://www.example.org/#hasName predicate P relates the object x to the object y. http://www.w3.org/1999/02/22-rdf-syntax-ns#type • Literals – Concrete data values • RDF offers only binary predicates (properties) – E.g.: "John Smith", "1", "2006-03-07"

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10 Resource Description Framework (RDF) – RDF Schema (RDFS) Graph Model

• The triple data model can be represented as a graph • RDF Schema (RDFS) is a language for capturing the semantics of a domain, for example: • Such graph is called in the Artificial Intelligence community a – In RDF: semantic net <#john, rdf:type, #Student> – What is a “#Student”? • Labeled, directed graphs – Nodes: resources, literals • RDFS is a language for defining RDF types: – Labels: properties – Edges: statements – Define classes: ex:father-of ex:john ex:bill • “#Student is a class” – Relationships between classes: • “#Student is a sub-class of #Person” ex:father-of – Properties of classes: • “#Person has a property hasName”

ex:tom

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RDF Schema (RDFS) RDF Schema (RDFS) - Example

• Classes: <#Student, rdf:type, #rdfs:Class> • Class hierarchies: <#Student, rdfs:subClassOf, #Person>

• Properties: <#hasName, rdf:type, rdf:Property> • Property hierarchies: <#hasMother, rdfs:subPropertyOf, #hasParent>

• Associating properties with classes (a): – “The property #hasName only applies to #Person” <#hasName, rdfs:domain, #Person> • Associating properties with classes (b): – “The type of the property #hasName is #xsd:string” <#hasName, rdfs:range, xsd:string>

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11 (OWL) OWL

• RDFS has a number of Limitations: • OWL is layered into languages of different expressiveness – Only binary relations – OWL Lite: Classification Hierarchies, Simple Constraints – Characteristics of Properties, e.g. inverse, transitive, symmetric – OWL DL: Maximal expressiveness while maintaining tractability – Local range restrictions, e.g. for class Person, the property hasName has range xsd:string – OWL Full: Very high expressiveness, loses tractability, all syntactic freedom of RDF – 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 • More expressive means harder to reason with

• The Web Ontology Language (OWL) provides an ontology language, that is • Different Syntaxes: a more expressive Vocabulary Definition Language for use with RDF – RDF/XML (Recommended for Serialization) – Class membership – N3 (Recommended for Human readable Fragments) – Equivalance of classes – Abstract Syntax (Clear Human Readable Syntax) lite – Consistency – Classification DL Full

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OWL – Example: The Wine Ontology OWL – Example: The Wine Ontology Schema

• 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/

[http://mysite.verizon.net/jflynn12/VisioOWL/VisioOWL.htm] 47 48

12 SPARQL – Querying RDF SPARQL Queries

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

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

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SPARQL Example Query 1 SPARQL Example Query 2

“Return the full names of all people in the graph” “Return the relation between John and Mary”

PREFIX vCard: PREFIX ex: SELECT ?fullName SELECT ?p WHERE {?x vCard:FN ?fullName} WHERE {ex:john ?p ex:mary}

@prefix ex: . result: @prefix ex: . @prefix vcard: . @prefix vcard: . result: ex:john ex:john vcard:FN "John Smith" ; vcard:FN "John Smith" ; vcard:N [ fullName vcard:N [ vcard:Given "John" ; vcard:Given "John" ; p vcard:Family "Smith" ] ; ======vcard:Family "Smith" ] ; ex:hasAge 32 ; ex:hasAge 32 ; ======ex:marriedTo :mary . "John Smith" ex:marriedTo :mary . ex:mary ex:mary vcard:FN "Mary Smith" ; "Mary Smith" vcard:FN "Mary Smith" ; vcard:N [ vcard:N [ vcard:Given "Mary" ; vcard:Given "Mary" ; vcard:Family "Smith" ] ; vcard:Family "Smith" ] ; ex:hasAge 29 . ex:hasAge 29 .

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13 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} @prefix ex: . result: @prefix vcard: . ex:john vcard:FN "John Smith" ; vcard:N [ y vcard:Given "John" ; vcard:Family "Smith" ] ; ILLUSTRATION BY LARGER ======ex:hasAge 32 ; ex:marriedTo :mary . ex:mary vcard:FN "Mary Smith" ; EXAMPLES vcard:N [ vcard:Given "Mary" ; vcard:Family "Smith" ] ; ex:hasAge 29 .

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Illustration 1 – KIM Browser Plugin Illustration 2 – Disco Hyperdata Browser

• KIM Browser Plugin Dereferencable Disco Hyperdata Browser Web content is annotated using URI navigating the Semantic Web as an ontologies unbound set of data sources 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

Annotated Content

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14 Extensions: Linked Open 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 EXTENSIONS together

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Extensions: Linked Open Data 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

Dieter Fensel

• 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)

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15 Extensions: Linked Open Data - GeoNames Extensions: Linked Open Data - DBpedia

• The GeoNames Ontology makes it possible to add geospatial semantic • DBpedia is a community effort to extract structured information from information to the Web of Data Wikipedia and to make this information available on the Web

• We can utilize GeoNames location within the FOAF profile • 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 Dieter Fensel Data – What is the population of the city in which Dieter Fensel lives? => 117916 people – At which elevation does Dieter Fensel live? • GeoNames is also linked to more datasets => 574m – US Census Data – Who is the mayor of the city in which Dieter Fensel lives – Movie (Linked MDB) => Hilde Zach – Extracted data from Wikipedia (DBpedia)

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SEMANTIC WEB - PROCESSES MOTIVATION

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16 Motivation 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

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

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Motivation 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

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17 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 TECHNICAL SOLUTIONS around Web processes/services

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Semantic Web Services Semantic Web Services

• Brings the benefits of Semantics to the executable part of the Web • Semantic Web Services are a layer on top of existing Web service – Ontologies as data model technologies and do not aim to replace them – Unambiguous definition of service functionality and external interface • Provide a formal description of services, while still being compliant with • Reduce human effort in integrating services in SOA existing and emerging technologies – Many tasks in the process of using Web services can be automated

• Improve dynamism • Distinguish between a Web service (computational entity) and a service – New services available for use as they appear (value provided by invocation) – Service Producers and Consumers don’t need to know of each others existence • Make Web services easier to: • Improve stability – Find – Service interfaces are not tightly integrated so even less impact from changes – Compare – Services can be easily replaced if they are no longer available – Compose – Failover possibilities are limited only by the number of available services – Invoke

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18 Technical Overview WSMO – Design Principles

Conceptual Model for Web Service versus Service SWS

Formal Language for WSMO

Strict Decoupling Ontology-Based of Modeling Elements WSMO Centrality of Ontological Role Mediation Separation

Execution Environment Description versus Implementation Ontology & Rule Language For SWS for the Semantic Web

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WSMO – Conceptual Model WSML – Language Family

Objectives that a client wants to achieve by using Web Services WSML - Full

Formally specified Semantic description terminology used of Web Services WSML - Rule by all other with • Capability (functional)‏ WSML - DL components • Interfaces (usage) f without

Expressivity WSML - Flight

Connectors between components with mediation facilities for handling heterogeneities WSML - Core

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19 Semantic Execution Environment Semantic Execution Environment - WSMX

Semanc Execuon Environment

vercal broker

Discovery Ranking Selecon

Composion Data Mediaon Process Mediaon

Process Liing &

Monitoring Execuon Lowering

base

Reasoning Storage

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Receives a customer id Illustration 1: SWS Challenge and returns a full customer description

id Purchase Order

cid

openOrder Purchase Order Confirmation addItem*

closeOrder

Blue Company can only send POs and ILLUSTRATION BY LARGER • Blue companyreceive has PO discovered Moon company on the Web Confirmations • Blue company wishes to communicate with MoonAllows the company opening of a PO, EXAMPLES the specification of the items to be purchased and the • Broker required to resolve data and process interoperabilityclosing of the POissues

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20 Illustration 2: Virtual Travel Agency Illustration 3: WSMX At Work

Rail Services

Flight Services

Hotel Services

Car Hire Services

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Illustration 3: WSMX At Work Illustration 3: WSMX At Work

Goal expressed Request to discover in WSML is sent to Web services. WSMX System Interface

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21 Illustration 3: WSMX At Work Illustration 3: WSMX At Work

Com. M. implements the interface to receive WSML goals

Com. M. informs Core that Goal has been received

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Illustration 3: WSMX At Work Illustration 3: WSMX At Work

Chor. wrapper picks up event for New choreography Chor. component Instance is created

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22 Illustration 3: WSMX At Work Illustration 3: WSMX At Work

Core is notified that choreography instance has been WSML goal is created. parsed to internal format.

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Illustration 3: WSMX At Work Illustration 3: WSMX At Work

Discovery is invoked for parsed goal. Discovery may requires ontology mediation.

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23 Illustration 3: WSMX At Work Illustration 3: WSMX At Work

After data mediation, Discovery iterates, Selection is invoked if needed through to relax result set to last steps until finally one service. result set is finished.

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Illustration 3: WSMX At Work Illustration 3: WSMX At Work

Result is returned to Com. Man. to be Choreography forwarded to the instance for goal service requester. requester is checked for next steps.

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24 Illustration 3: WSMX At Work Illustration 3: WSMX At Work

Set of Web Service descriptions expressed in requester’s own Set of Web Service format returned to descriptions goal requester. expressed in WSML sent to adapter.

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Extensions: Mobile Services

EXTENSIONS

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25 Extensions: Mobile Services Extensions: Intelligent Cars

• 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 101 102

Extensions: Intelligent Cars Extensions: A Smarter Electricity Grid

• 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 From: http://www.nff.tu-bs.de/index.php?id=464&L=1 – Enabling interoperability between systems

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26 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” SUMMARY – 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”

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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 REFERENCES • WSML for describing services at different levels of expressivity • WSMX as a Semantic Execution Environment for bringing requesters and providers together

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27 References References

– David Martin, et al., OWL-S: Semantic Markup for Web Services, W3C Member Submission • Mandatory reading: 22 November 2004, http://www.w3.org/Submission/OWL-S. – T. Berners-Lee, J. Hendler, O. Lassila. The Semantic Web, Scientific American, 2001. – Joel Farrell and Holger Lausen, Semantic Annotations for WSDL and XML Schema, W3C – Dieter Fensel, Holger Lausen, Axel Polleres, Jos de Bruijn, Michael Stollberg, Dumitru Recommendation 28 August 2007, http://www.w3.org/TR/sawsdl Roman, John Domingue, Enabling Semantic Web Services: The Web Service Modeling – Rama Akkiraju et al., Web Service Semantics - WSDL-S, W3C Member Submission 7 Ontology, Springer-Verlag, 2007 November 2005, http://www.w3.org/Submission/WSDL-S • Further reading: – Steve Battle et al., Semantic Web Services Framework (SWSF), W3C Member Submission 9 September 2005, http://www.w3.org/Submission/SWSF – Dieter Fensel, Mick Kerrigan, Michal Zaremba (Eds.), Implementing Semantic Web Services: The SESA Framework. Springer-Verlag, 2008. – Semantic Web Primer: http://www.ics.forth.gr/isl/swprimer/ – Jos de Bruijn, Dieter Fensel, Mick Kerrigan, Uwe Keller, Holger Lausen, and James – RDF Primer: http://www.w3.org/TR/REC-rdf-syntax/ Scicluna: Modeling Semantic Web Services, Springer-Verlag, 2008 – RDF Schema: http://www.w3.org/TR/rdf-schema/ – D. Fensel. Ontologies: A Silver Bullet for and Electronic – OWL Guide: http://www.w3.org/TR/owl-guide/ Commerce, 2nd Edition, Springer 2003. – RDF SPARQL Protocol: http://www.w3.org/TR/rdf-sparql-protocol/ – G. Antoniou and F. van Harmelen. A Semantic Web Primer, (2nd edition), The MIT Press 2008. – Linked Open Data: http://linkeddata.org/ – H. Stuckenschmidt and F. van Harmelen. Information Sharing on the Semantic Web, – Linked Open Data Tutorial: http://www4.wiwiss.fu-berlin.de/bizer/pub/LinkedDataTutorial/ Springer 2004. – WSMO: http://www.wsmo.org/TR/d2/ – T. Berners-Lee. Weaving the Web, HarperCollins 2000 – WSML: http://www.wsmo.org/TR/d16/d16.1/ – T.R. Gruber, Toward principles for the design of ontologies used or knowledge sharing? , Int. – WSMO/WSML Tutorials: http://wiki.sti2.at/index.php?title=WSMT_Tutorials J. Hum.-Comput. Stud., vol. 43, no. 5-6,1995

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References

• Wikipedia links: – URI: http://en.wikipedia.org/wiki/URI – RDF; http://en.wikipedia.org/wiki/Resource_Description_Framework – RDFS: http://en.wikipedia.org/wiki/RDFS – SPARQL: http://en.wikipedia.org/wiki/SPARQL – WSMO: http://en.wikipedia.org/wiki/WSMO – WSML: http://en.wikipedia.org/wiki/Web_Services_Modeling_Language

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

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28 Questions?

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