1. Internet Engineering Course
Semantic Web,
Web Services,
Semantic Web Services

2. Agenda Vision of Next Generation Web Technology Semantic Web
Today’s Web
The Semantic Web Impact
Semantic Web Technologies
A Layered Approach
Web Services
Why Web Services?
Enabling Technologies
Web Service Composition
Main Issues concerning the composition
Semantic Web Services

3. Vision of Next Generation Web Technologies
500 million users
more than 3 billion pages
WWW
URI, HTML, HTTP
Static 3

4. Vision of Next Generation Web Technologies
Serious Problems in
information finding,
information extracting,
information representing,
information interpreting and
and information maintaining.
WWW
URI, HTML, HTTP
Semantic Web
RDF, RDF(S), OWL
Static 4

5. Vision of Next Generation Web Technologies
Web Services
UDDI, WSDL, SOAP
Dynamic
Bringing the computer back as a device for computation
WWW
URI, HTML, HTTP
Semantic Web
RDF, RDF(S), OWL
Static 5

6. Vision of Next Generation Web Technologies
Bringing the web to its full potential
Semantic Web
Services
Web Services
UDDI, WSDL, SOAP
Dynamic
WWW
URI, HTML, HTTP
Semantic Web
RDF, RDF(S), OWL
Static 6

7. Semantic Web

8. Semantic Web Outline Today’s Web The Semantic Web Impact
Semantic Web Technologies
A Layered Approach

9. 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 catalogues of online stores and ordering products by filling out forms

10. 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

11. Problems of Keyword-Based Search Engines
High recall, low precision.
Low or no recall
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

12. 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, . . .

13. 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

14. Semantic Web Outline Today’s Web The Semantic Web Impact
Semantic Web Technologies
A Layered Approach

15. The Semantic Web Impact – Knowledge Management
Knowledge management concerns itself with acquiring, accessing, and maintaining knowledge within an organization
Key activity of large businesses: internal knowledge as an intellectual asset
It is particularly important for international, geographically dispersed organizations
Most information is currently available in a weakly structured form (e.g. text, audio, video)

16. Limitations of Current Knowledge Management Technologies
Searching information
Keyword-based search engines
Extracting information
human involvement necessary for browsing, retrieving, interpreting, combining
Maintaining information
inconsistencies in terminology, outdated information.
Viewing information
Impossible to define views on Web knowledge

17. 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.

18. The Semantic Web Impact – B2C Electronic Commmerce
A typical scenario: user visits one or several online shops, browses their offers, selects and orders products.
Ideally humans would visit all, or all major online stores; but too time consuming
Shopbots are a useful tool

19. Limitations of Shopbots
They rely on wrappers: extensive programming required
Wrappers need to be reprogrammed when an online store changes its outfit
Wrappers extract information based on textual analysis
Error-prone
Limited information extracted

20. Semantic Web Enabled B2C Electronic Commerce
Software agents that can interpret the product information and the terms of service.
Pricing and product information, delivery and privacy policies will be interpreted and compared to the user requirements.
Information about the reputation of shops
Sophisticated shopping agents will be able to conduct automated negotiations

21. The Semantic Web Impact – B2B Electronic Commerce
Greatest economic promise
Currently relies mostly on EDI
Isolated technology, understood only by experts
Difficult to program and maintain, error-prone
Each B2B communication requires separate programming
Web appears to be perfect infrastructure
But B2B not well supported by Web standards

22. Semantic Web Enabled B2B Electronic Commerce
Businesses enter partnerships without much overhead
Differences in terminology will be resolved using standard abstract domain models
Data will be interchanged using translation services.
Auctioning, negotiations, and drafting contracts will be carried out automatically (or semi-automatically) by software agents

23. Semantic Web Outline Today’s Web The Semantic Web Impact
Semantic Web Technologies
A Layered Approach

24. Semantic Web Technologies
Explicit Metadata
Ontologies
Logic and Inference
Agents

25. On HTML
Web content is currently formatted for human readers rather than programs
HTML is the predominant language in which Web pages are written (directly or using tools)
Vocabulary describes presentation

26. An HTML Example <h1>Agilitas Physiotherapy Centre</h1>
Welcome to the home page of the Agilitas Physiotherapy Centre. Do
you feel pain? Have you had an injury? Let our staff Lisa Davenport,
Kelly Townsend (our lovely secretary) and Steve Matthews take care
of your body and soul.
<h2>Consultation hours</h2>
Mon 11am - 7pm<br>
Tue 11am - 7pm<br>
Wed 3pm - 7pm<br>
Thu 11am - 7pm<br>
Fri 11am - 3pm<p>
But note that we do not offer consultation during the weeks of the
<a href=". . .">State Of Origin</a> games.

27. Problems with HTML Humans have no problem with this
Machines (software agents) do:
How distinguish therapists from the secretary,
How determine exact consultation hours
They would have to follow the link to the State Of Origin games to find when they take place.

28. A Better Representation
<company>
<treatmentOffered>Physiotherapy</treatmentOffered>
<companyName>Agilitas Physiotherapy Centre</companyName>
<staff>
<therapist>Lisa Davenport</therapist>
<therapist>Steve Matthews</therapist>
<secretary>Kelly Townsend</secretary>
</staff>
</company>

29. Explicit Metadata
This representation is far more easily processable by machines
Metadata: data about data
Metadata capture part of the meaning of data
Semantic Web does not rely on text- based manipulation, but rather on machine-processable metadata

30. Ontologies The term ontology originates from philosophy
The study of the nature of existence
Different meaning from computer science
An ontology is an explicit and formal specification of a conceptualization

31. Typical Components of Ontologies
Terms denote important concepts (classes of objects) of the domain
e.g. professors, staff, students, courses, departments
Relationships between these terms: typically class hierarchies
a class C to be a subclass of another class C' if every object in C is also included in C'
e.g. all professors are staff members

32. Further Components of Ontologies
Properties:
e.g. X teaches Y
Value restrictions
e.g. only faculty members can teach courses
Disjointness statements
e.g. faculty and general staff are disjoint
Logical relationships between objects
e.g. every department must include at least 10 faculty

33. Ontology Example Concept Property Relation Axiom
name
Concept
conceptual entity of the domain
Property
attribte describing a concept
Relation
relationship between concepts or properties
Axiom
coherency description between Concepts / Properties / Relations via logical expressions
Person
Field
research
field
isA – hierarchy (taxonomy)
Student
Professor
attends
holds
Lecture
Syllabus
topic
holds(Professor, Lecture) =>
Lecture.topic = Professor.researchField

34. The Role of Ontologies on the Web
Ontologies provide a shared understanding of a domain: semantic interoperability
overcome differences in terminology
mappings between ontologies
Ontologies are useful for the organization and navigation of Web sites

35. The Role of Ontologies in Web Search
Ontologies are useful for improving the accuracy of Web searches
search engines can look for pages that refer to a precise concept in an ontology
Web searches can exploit generalization/ specialization information
If a query fails to find any relevant documents, the search engine may suggest to the user a more general query.
If too many answers are retrieved, the search engine may suggest to the user some specializations.

36. Web Ontology Languages
RDF Schema
RDF is a data model for objects and relations between them
RDF Schema is a vocabulary description language
Describes properties and classes of RDF resources
Provides semantics for generalization hierarchies of properties and classes

37. Web Ontology Languages (2)
OWL
A richer ontology language
relations between classes
e.g., disjointness
cardinality
e.g. “exactly one”
richer typing of properties
characteristics of properties (e.g., symmetry)

38. Logic and Inference
Logic is the discipline that studies the principles of reasoning
Formal languages for expressing knowledge
Well-understood formal semantics
Declarative knowledge: we describe what holds without caring about how it can be deduced
Automated reasoners can deduce (infer) conclusions from the given knowledge

39. An Inference Example prof(X)  faculty(X) faculty(X)  staff(X)
prof(michael)
We can deduce the following conclusions:
faculty(michael)
staff(michael)
prof(X)  staff(X)

40. Logic versus Ontologies
The previous example involves knowledge typically found in ontologies
Logic can be used to uncover ontological knowledge that is implicitly given
It can also help uncover unexpected relationships and inconsistencies
Logic is more general than ontologies
It can also be used by intelligent agents for making decisions and selecting courses of action

41. Tradeoff between Expressive Power and Computational Complexity
The more expressive a logic is, the more computationally expensive it becomes to draw conclusions
Drawing certain conclusions may become impossible if non- computability barriers are encountered.
Our previous examples involved rules “If conditions, then conclusion,” and only finitely many objects
This subset of logic is tractable and is supported by efficient reasoning tools

42. Inference and Explanations
Explanations: the series of inference steps can be retraced
They increase users’ confidence in Semantic Web agents: “Oh yeah?” button
Activities between agents: create or validate proofs

43. Typical Explanation Procedure
Facts will typically be traced to some Web addresses
The trust of the Web address will be verifiable by agents
Rules may be a part of a shared commerce ontology or the policy of the online shop

44. Software Agents Software agents work autonomously and proactively
They evolved out of object oriented and compontent-based programming
A personal agent on the Semantic Web will:
receive some tasks and preferences from the person
seek information from Web sources, communicate with other agents
compare information about user requirements and preferences, make certain choices
give answers to the user

45. Semantic Web Agent Technologies
Metadata
Identify and extract information from Web sources
Ontologies
Web searches, interpret retrieved information
Communicate with other agents
Logic
Process retrieved information, draw conclusions

46. Semantic Web Agent Technologies (2)
Further technologies (orthogonal to the Semantic Web technologies)
Agent communication languages
Formal representation of beliefs, desires, and intentions of agents
Creation and maintenance of user models.

47. Semantic Web Outline Today’s Web The Semantic Web Impact
Semantic Web Technologies
A Layered Approach

48. A Layered Approach
The development of the Semantic Web proceeds in steps
Each step building a layer on top of another
Principles:
Downward compatibility
Upward partial understanding

49. The Semantic Web Layer Tower

50. Semantic Web Layers XML layer RDF layer Ontology layer Syntactic basis
RDF basic data model for facts
RDF Schema simple ontology language
Ontology layer
More expressive languages than RDF Schema
Current Web standard: OWL

51. Semantic Web Layers (2) Logic layer Proof layer Trust layer
enhance ontology languages further
application-specific declarative knowledge
Proof layer
Proof generation, exchange, validation
Trust layer
Digital signatures
recommendations, rating agencies ….

52. Web Services

53. Agenda What are Web Services? Why Web Services? Enabling Technologies?
What is Web Service Composition?
Main Issues concerning the composition? 53

54. Web Evolution XML 54 HTML Connectivity Presentation Programmability
Technology
TCP/IP
Connectivity
Presentation
Programmability
FTP, , Gopher
Innovation
Web Pages
Browse the Web
Web Services
Program the Web 54

55. What are Web Services? Definition from W3C
"Web Service is a software application identified by a URI, whose interfaces and bindings are capable of being defined, described, and discovered by XML artifacts and which supports direct interactions with other software applications using XML-based messages via internet-based protocols". 55

56. What are Web Services? Every component that works in a network,
is modular
is self-descriptive,
provides services independent of platform and application,
conforms to an open set of standards and
follows a common structure for description and invocation. 56

57. Why Web Services Interoperability. Ubiquity.
Any WS can interact with any other WS.
Ubiquity.
Any device which supports HTTP + XML can host & access WS.
Effortless entry in this concept.
easily understood + free toolkits
Industry Support.
major vendors support surrounding technology. 57

58. Web Services Architecture
Components
Service Providers
Service Brokers
Service Requestors
Operations
Publish / Unpublish
Find
Bind 58

59. 59

60. Enabling technologies
They encapsulate a set of standards that allow the developers to implement distributed applications.
SOAP (Simple Object Access Protocol),
XML messaging protocol for basic service interoperability
WSDL (Web Service Description Language)
Common grammar for describing services
UDDI (Universal Description Discovery and Integration)
infrastructure required to publish and discover services. 60

61. SOAP Uniform way of The requestor sends a msg to the service
passing XML-encoded data.
performing RPCs over SMTP, FTP, TCP/IP, HTTP
The requestor sends a msg to the service
The service processes the msg.
The service sends back a response.
The requestor has no knowledge of how the service is implemented. 61

62. SOAP Example
<SOAP-ENV:Envelope xmlns:SOAP-ENV=" SOAP-ENV:encodingStyle="
<SOAP-ENV:Body>
<e:Book> <title>My Life and Work</title>
<firstauthor href="#Person-1"/>
<secondauthor href="#Person-2"/>
</e:Book>
<e:Person id="Person-1"><name>Henry Ford</name>
<address xsi:type="m:Electronic-address"> <web>
</address>
</e:Person>
<e:Person id="Person-2"> <name>Samuel Crowther</name> <address xsi:type="n:Street-address">
<street>Martin Luther King Rd</street>
<city>Raleigh</city>
<state>North Carolina</state>
</SOAP-ENV:Body> </SOAP-ENV:Envelope> 62

63. SOAP - RPC Must define an RPC protocol
How will types be transported (in XML) and how application represents them.
RPC parts (object id, operation name, parameters)
SOAP assumes a type system based on XML-schema. 63

64. SOAP Example - doGoogleSearch
<SOAP-ENV:Envelope xmlns:SOAP-ENV= xmlns:xsi=" xmlns:xsd="
<SOAP-ENV:Body>
<ns1:doGoogleSearch xmlns:ns1="urn:GoogleSearch" SOAP- ENV:encodingStyle="
<key xsi:type="xsd:string"> </key>
<q xsi:type="xsd:string">my query</q>
<start xsi:type="xsd:int">0</start>
<maxResults xsi:type="xsd:int">10</maxResults>
<filter xsi:type="xsd:boolean">true</filter>
<restrict xsi:type="xsd:string"/>
<safeSearch xsi:type="xsd:boolean">false</safeSearch>
<lr xsi:type="xsd:string"/>
<ie xsi:type="xsd:string">latin1</ie>
<oe xsi:type="xsd:string">latin1</oe>
</ns1:doGoogleSearch>
</SOAP-ENV:Body>
</SOAP-ENV:Envelope> 64

65. SOAP Example - doGoogleSearchResult
<SOAP-ENV:Envelope xmlns:SOAP-ENV=" ………..
<SOAP-ENV:Body>
<ns1:doGoogleSearchResponse xmlns:ns1="urn:GoogleSearch" SOAP- ENV:encodingStyle="
<return xsi:type="ns1:GoogleSearchResult">
<documentFiltering xsi:type="xsd:boolean">false</documentFiltering>
<estimatedTotalResultsCount xsi:type="xsd:int">3</estimatedTotalResultsCount>
<directoryCategories xmlns:ns2=" xsi:type="ns2:Array" ns2:arrayType="ns1:DirectoryCategory[0]"/>
<searchTime xsi:type="xsd:double"> </searchTime>
<resultElements xmlns:ns3=" xsi:type="ns3:Array" ns3:arrayType="ns1:ResultElement[3]">
<item xsi:type="ns1:ResultElement">
<cachedSize xsi:type="xsd:string">12k</cachedSize>
<directoryCategory xsi:type="ns1:DirectoryCategory">Category</directoryCategory>
<relatedInformationPresent xsi:type="xsd:boolean">true</relatedInformationPresent>
<directoryTitle xsi:type="xsd:string"/>
<summary xsi:type="xsd:string"/>
<URL xsi:type="xsd:string">
<title xsi:type="xsd:string"><b>SHRDLU</b></title>
</item> 65

66. WSDL IDL of Web Services XML format developed by IBM & MS.
Provides two types of information
Abstract interface: Application-level service description
Protocol dependent details 66

67. WSDL - Abstract interface
Messages exchanged in an interaction.
Components:
Vocabulary (XSD for type definition)
Message: abstract, typed data definition sent to and from services.
Interaction 67

68. Vocabulary 68 <wsdl:types>
<xsd:schema xmlns=" targetNamespace="urn:GoogleSearch">
<xsd:complexType name="GoogleSearchResult">
<xsd:all>
<xsd:element name="documentFiltering" type="xsd:boolean"/>
<xsd:element name="searchComments" type="xsd:string"/>
<xsd:element name="estimatedTotalResultsCount" type="xsd:int"/>
<xsd:element name="estimateIsExact" type="xsd:boolean"/>
<xsd:element name="resultElements" type="typens:ResultElementArray"/>
<xsd:element name="searchQuery" type="xsd:string"/>
<xsd:element name="startIndex" type="xsd:int"/>
<xsd:element name="endIndex" type="xsd:int"/>
<xsd:element name="searchTips" type="xsd:string"/>
<xsd:element name="directoryCategories" type="typens:DirectoryCategoryArray"/>
<xsd:element name="searchTime" type="xsd:double"/>
</xsd:all>
</xsd:complexType> 68

69. Message 69 <message name="doGoogleSearch">
<part name="key" type="xsd:string"/>
<part name="q" type="xsd:string"/>
<part name="start" type="xsd:int"/>
<part name="maxResults" type="xsd:int"/>
<part name="filter" type="xsd:boolean"/>
<part name="restrict" type="xsd:string"/>
<part name="safeSearch" type="xsd:boolean"/>
<part name="lr" type="xsd:string"/>
<part name="ie" type="xsd:string"/>
<part name="oe" type="xsd:string"/>
</message>
<message name="doGoogleSearchResponse">
<part name="return" type="typens:GoogleSearchResult"/> 69

70. Interaction
<binding name="GoogleSearchBinding" type="typens:GoogleSearchPort">
<soap:binding style="rpc“ transport="
<operation name="doGetCachedPage">
<soap:operation soapAction="urn:GoogleSearchAction"/>
<input>
<soap:body use="encoded"
encodingStyle=" namespace="urn:GoogleSearch"/>
</input>
<output>
<soap:body use="encoded" encodingStyle=" namespace="urn:GoogleSearch"/>
</output>
</operation> 70

71. UDDI Global business registry Root under www.uddi.org
Three types of information
White pages
Yellow pages
Green pages 71

72. UDDI information model
PublisherAssertion
Info about relationship between 2 parties
BusinessEntity
Info about business that publishes
Info about service
encapsulates
BusinessService
Descriptive info about a service
encapsulates
tModel
Descriptions on specifications of services
BindingTemplate
Technical info about a service end point 72

73. Web Service Composition
Definition: Technique of composing the functionalities of relatively simpler services to produce a ‘meaningful’ arbitrarily complex application. 73

74. WS composition - Classification
Proactive Composition & Reactive Composition
Proactive: offline composition of available services
When: services are stable and always running
Example: ticket reservation service
Reactive: dynamically creating a composite service.
When: composite service not often used and service processes not stable.
Example: tour manager where the itinerary is not predefined 74

75. WS composition – Classification (2)
Mandatory & Optional-Composite Services
Mandatory: all subcomponents must participate to yield a result
Example: service that calculates the averages of stock values for a company.
Optional: subcomponents are not obligated to participate for a successful execution.
Example: services that include a subcomponent that is an optimizer. 75

76. Important issues on WS composition
Service Discovery
Service Coordination and Management
Uniform Information Exchange Infrastructure
Fault Tolerance and Scalability
Adaptiveness
Reliability & Transactions
Security
Accountability
Testing 76

77. Service Discovery An efficient discovery structure should be able:
find out all services implementing some functionality (ontology)
semantic level reasoning (discover most appropriate service).
scalable.
Most of existing discovery infrastructures use a central lookup server (Jini, UPnP)
Semantic Language: DAML-S, a process modelling language for computer-interpretable description of services.
AI inspired description logic-based language, built on top of XML + RDF for well-defined semantics and a set of language constructs and properties. 77

78. Service Discovery - DAML-S
Enables automatic Web Service discovery. =automatic location of services with required functionality.
Currently performed manually
DAML-S: expressed in computer interpretable semantic markup. 78

79. Service Discovery - Example of DAML-S
<daml:Class rdf: ID=”CompositeProcess”>
<daml:intersectionOf rdf>parseType = “daml:collection”>
<daml:Class rdf:about=”#Process”/>
<daml:Restriction daml:minCardinality=”1”>
<daml:onProperty rdf:resource=”#composedOf”/>
</daml:Restriction>
</daml:intersectionOf>
</daml:Class>
<rdf:Property rdf:ID=”composedOf”>
<rdfs: domain rdf:resource=”#CompositeProcess”/>
<rdfs: range rdf:resource=”#ControlConstruct”/>
</rdf:Property> 79

80. Reliability & Transactions
How we can measure reliability?
WS descriptions may lie!
Transactions are fundamental to reliable distributed computing.
Traditional transaction systems support ACID semantics, use a two-phase commit approach: all participating resources are locked until entire transaction is completed.
Only in close environments where transactions are short-lived
Not on an open environment (flexibility in how it is attained)
MS XLANG: compensating transactions.
Split the model into concurrent sub-transactions that can commit independently (requires compensation over committed sub transactions in case of abortion). 80

81. Security Basic security: HTTP over SSL Authorisation control.
Existing authorisation control frameworks not applicable to WS (designed for some services e.g. network access control (DIAMETER) or not well designed to access different administrative domains (.NET Passport))
Proposal: generic authorisation control protocol based on SOAP/XML. Supports credential transformation.
Need for CA in each domain. It will issue users and services with certificate and secret key pairs used for user authentication and request signing.
Credentials described in an XML-based language. Authorisation server validates the certificate, credentials etc. If everything is successfully validated, the authorisation server sends back a SOAP response containing the result. 81

82. Semantic Web Services

83. Semantic Web Technology Web Service Technology
Semantic Web Services
Semantic Web Technology +
Web Service Technology
allow machine supported data interpretation
ontologies as data model
automated discovery, selection, composition,
and web-based execution of services
=> Semantic Web Services as integrated solution for realizing Vision of Next Generation Web Technologies of the next generation of the Web 83

84. Semantic Web Services
define exhaustive description frameworks for describing Web Services and related aspects (Web Service Description Ontologies)
support ontologies as underlying data model to allow machine supported data interpretation (Semantic Web aspect)
define semantically driven technologies for automation of the Web Service usage process (Web Service aspect) 84

85. Semantic Web Services Usage Process:
Publication: Make available the description of the capability of a service
Discovery: Locate different services suitable for a given task
Selection: Choose the most appropriate services among the available ones
Composition: Combine services to achieve a goal
Mediation: Solve mismatches (data, protocol, process) among the combined
Execution: Invoke services following programmatic conventions 85

86. Semantic Web Services Execution support:
Monitoring: Control the execution process
Compensation: Provide transactional support and undo or mitigate unwanted effects
Replacement: Facilitate the substitution of services by equivalent ones
Auditing: Verify that service execution occurred in the expected way 86

87. Additional Reading (Semantic Web)
Johan Hjelm, “Creating the Semantic Web with RDF”, John Wiley, 2001
Dieter Fensel: “Ontologies: A Silver Bullet for Knowledge Management and Electronic Commerce”, Springer Verlag, 2001
John Davies, Dieter Fensel & Frank van Harmelen:, “Towards the Semantic WEB – Ontology Driven Knowledge Management”, John Wiley, 2002
Dieter Fensel, Wolfgang Wahlster, Henry Lieberman, James Hendler (Eds.): “Spinning the Semantic Web: Bringing the World Wide Web to Its Full Potential”, MIT Press, 2002
Michael C. Daconta, Leo J. Obrst, Kevin T. Smith: “The Semantic Web: A Guide to the Future of XML, Web Services, and Knowledge Management”, John Wiley, 2003
Thomas B. Passin, "Explorer's Guide to the Semantic Web", ISBN , June 2004
Jeff Pollock and Ralph Hodgson,
"Adaptive Information: Improving Business Through Semantic Interoperability, Grid Computing, and Enterprise Integration“, Wiley Computer Publishing, September 2004
M. Klein and B. Omelayenko (eds.), “Knowledge Transformation for the Semantic Web”, Vol. 95, Frontiers in Artificial Intelligence and Applications, IOS Press, 2003 87

88. Additional Reading (Web Services)
Dipanjan Chakraborty, Technical Report TR-CS-01-19: Dynamic Service composition: State-of-the-Art and Research Directions. University of Maryland, Baltimore County, 2001.
Anans Rajamam, “Overview of UDDI”, Online, 2001.
F.Curbera and al, “Unraveling the Web Services Web: An Introduction to SOAP, WSDL, and UDDI”. IEEE Internet Computing March-April 2002, p
DAML Service Coalition, DAML-S Semantic Markup for Web Services. Online at
WSDL Specification, Online at
Steve Vinoski, Web Services and Dynamic Discovery, Online at
UDDI Specification, Online at
Sheila A. McIlaith, Tran Cao Son, Honglei Zeng, Semantic Web Services, IEEE Intelligent Systems, 2001
Vladimir Tosic, Bernard Pagurek, Babak Esfandiari, Kruti Patel, On the Management of Composition of Web Services, Carleton University, Canada.
Tom Clements, “Overview of SOAP”. Online at:
Deitel,”Web Services: A technical Introduction”, Prentice Hall, 2002. 88