EASTERN MEDITERRANEAN UNIVERSITY

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Intelligent Semantic Web Service Finder (ISWSF)
System

• EASTERN MEDITERRANEAN UNIVERSITY
• COMPUTER ENGINEERING DEPARTMENT

Presented By Duygu CELIK
Supervised By Atilla ELCI
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I. INTODUCTION

• The shortage of semantic parts, increasing number
  of web services in the web, and syntactic-based
  search operation are the main problems of current
  Web service technologies.
• These problems make discovering of appropriate
  web services challenging.
• This study provides an implementation of the
  intelligent semantic web service finder (ISWSF)
  system 5 that can be used to find related
  Semantic Web Services based on an clients
  request through a semantic search agent (SSA)

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Solution (cont)

• The system understands ontologies, concepts of
  Semantic Web Services and connects to each of
  these services.
• Has ability to extracts necessary profile
  information of services for choosing best
  Semantic Web Services (SWS) based on the client
  request.

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II. System parts (ISWSF)

• Client Interface
• Semantic Web Services (SWS) and OWL-S Files
• Semantic Search Agent
• UDDI Registry (Universal Description, Discovery
  and Integration. )
• Ontology Database (OWL Files)

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VI. IMPLEMENTATION

• JAVA Net Beans 4.0
• Java Server Pages (JSP)
• Java Classes
• Jena 2.0 Ontology API
• OWL-S API
• Protégé 3.1 (Ontology creator/edit tool)
• Created OWL Files
• Created OWL-S Files

Figure 5. Used modules for implementation of
ISWSF.
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2.1 What is Semantic Web Service and OWL-S

• SWS provides machine-readable descriptions of web
  services, which enable automated discovery,
  negotiation with, composition, execution, and
  monitoring of web services.
• OWL-S and Relation of SWS
• Ontology languages provide a way for describing
  their formal specification and have the ability
  to define properties of the Web Services (OWL-S)
• Through property description capabilities of
  ontology languages, descriptions of services can
  be defined more accurately and services can be
  related to other services or resources more
  easily.
• This study attempts to motivate OWL-S for
  discovering appropriate Web Services.

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2.2 What is UDDI?

• UDDI is a standard for discovery of Web Services
  that stands for Universal Description, Discovery
  and Integration.
• The UDDI registry can be classified into two
  categories which are UDDI-Web Services and
  UDDI-Business Registry.
• The Web Service developers publish their web
  services to the UDDI registry.
• Once published, the UDDI registry holds
  information of web service description and
  address.
• The UDDI allows clients to search this registry,
  find the planned service and retrieve its details.

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III. System Tasks

• System has two tasks
• Semantic Enhancement of clients input terms
• performs enhancement of the terms entered by the
• client with its synonym(s) or is_a relations.
• 2. Matching Step
• The system checks if there is a match between
  clients request info and the profile information
  (ontologies) of SWSs.

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IV. Survey

• The proposed system combines further aspects of
  two research topics
• Smart Web Query Engine1
• Matchmaking Algorithm of OWL-S/UDDI
  Matchmaker2,3,4 and 5
• To facilitate the finding of semantic web
  services for a client.

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V. SYSTEM ARCHITECTURE
A Client
UDDI Registry (Stores Web Services Information
Addresses)
Ontology Database (created in OWL) For Domain
SSA
Semantic Web Services (retrieve from UDDI)
Figure 1. The System Architecture of ISWSF.
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A. Semantic Enhancement of Input Terms
Figure 2. Semantic Enhancement of Input Term(s).
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B. Matching Step
Figure 3. Matching Step.
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6.2 Created OWL Files in Protégé 3.1

• The Protégé 3.1 used for creating ontologies and
  its classes, individuals and properties.
• The system is able to use those ontologies for
  retrieve relations between terms. (Vehicle Ont.)

Figure 6. Created the Vehicle Ontology in Protégé
3.1 and system used it for Retrieve relations
between Terms.
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What are rules for matching operation?

• separate rules as exact, plug in, subsumes and
  fail
• outR is one output of the request
• outA is output of the advertisement

Figure 4. Rules for Degree of Match Assignment,
1
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The WordNet 12, which was developed by the
Cognitive Science Laboratory at Princeton
University under the direction of Professor
George A. Miller.

• WordNet is an online lexical reference system.
• English nouns, verbs, adjectives and adverbs are
  arranged into synonym sets, each synonym set is
  representing one primary lexical concept. Various
  relations connect the synonym sets.
• A set of words that can be observed as exacting
  synonyms is called a Synset. For a queried word,
  finding the different synsets that contain the
  word, and finding related synsets.

Figure 7. Search the term Car in WordNet
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6.2 How create OWL-S (Semantic Web Services)
files?

• A new semantic technology OWL-S, can be used to
  develop SWSs descriptions. In this project we use
  OWL-S and OWL-S Editor and this OWL-S Editor aims
  to create an easy-to-use editor for creating
  OWL-S Ontology files for services.
• The editor is a Tab Widget plug-in for Protégé
  The ISWSF system used the inputs and outputs of
  the Profile classes of the SWS and then compare
  them with client inputs and outputs.

Figure 8. A Part of the Online Car Parts Selling
Service in OWL-S Editor (In Profile Information)
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Engineering Department of Eastern Mediterranean
University
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VII.CASE STUDY

• A client looking for a Online Car Parts Selling
  Service
• Client enters the term CAR in to ISWSF
• The system tries to find which ontologies include
  the term
• Client selects the related ontology name
• The system will operate on only that Ontology
  anymore
• Client enters I/O parameters
• The system will begin to perform Matching Step
• Compare between each client terms with each
  profile information in OWL-S files for each SWSs

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Vehicle Example
A Vehicle example A client is looking for
Online Car Parts Selling Service Search
Key Car INPUTS Car Parts OUTPUTS Price
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Vehicle Example (Cont.)
Figure 9. The client enters number of key words
for search operation
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Vehicle Example (Cont.)
Figure 10. The client enters Car term.
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Vehicle Example (Cont.)
Figure 11. The system found the related
ontologies.
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Vehicle Example (Cont.)
Figure 12. The system shows the results of the
"Car" terms synonym and is_a terms.
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Vehicle Example (Cont.)
Figure 13. The system is showing the results of
the Car" terms synonym terms.
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Engineering Department of Eastern Mediterranean
University
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Vehicle Example (Cont.)
Figure 14. Client enters I/O informations.
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University
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Vehicle Example (Cont.)
Figure 15. Returned results of matchmaking step.
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Engineering Department of Eastern Mediterranean
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Vehicle Example (Cont.)
Figure 16. SSA returned best SWS for client after
matching process.
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CONCLUSION

• The system performs two main functions, namely,
  enhancing client request term with synonym or
  relational terms and matchmaking step between the
  SWS I/O and client I/O.
• The system specifies relationships between those
  input/output parameters as EXACT, PLUGIN, SUBSUME
  and FAIL. According to those relationships the
  system serves appropriate web services to the
  client based on the client request.

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Engineering Department of Eastern Mediterranean
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REFERENCES

• 1 http//www.w3.org/TR/soap/
• 2 http//www.w3.org/TR/wsdl.html
• 3 http//www.uddi.org/
• 4 http//owlseditor.semwebcentral.org/
• 5 D. Çelik and A. Elçi A Semantic search agent
  approach Finding appropriate semantic Web
  services based on user request term(s), Enabling
  Technologies for the New Knowledge Society, ITI
  3rd International Conference on Information
  Communication Technology (ICICT 2005), 5-6
  December 2005, Cairo, Egypt. Proc. IEEE publ. to
  appear, http//www.icict.gov.eg/ICICT2005/

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REFERENCES (Cont.)

• 6 Roger H.L. Chiang, Cecil Eng Huang Chua and
  Veda C. Storey, A smart web query method for
  semantic retrieval of web data, Data Knowledge
  Engineering, Volume 38, Issue 1,July2001,Pages63-8
  4, http//www.sciencedirect.com/sciencehttp//www.
  kns.dlr.de/Projects/spreading/spreading190198.html
  .
• 7 K. Sycara, M. Paolucci, A. Ankolekar and N.
  Srinivasan, "Automated Discovery, Interaction and
  Composition of Semantic Web services," in Journal
  of Web Semantics, Volume 1, Issue 1, September
  2003, pp. 27-46.
• 8 N. Srinivasan, M. Paolucci and K. Sycara,
  "Adding OWL-S to UDDI, implementation and
  throughput."First International Workshop on
  Semantic Web Services and Web Process Composition
  (SWSWPC 2004) 6-9, 2004, San Diego, California,
  USA.

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REFERENCES (Cont.)

• 9 Massimo Paolucci, Takahiro Kawamura, Terry R.
  Payne, Katia Sycara "Importing the Semantic Web
  in UDDI," in Proceedings of Web Services,
  E-business and Semantic Web Workshop
• 10 T. Kawamura, J. A. De Blasio, T. Hasegawa,
  M. Paolucci, and K. Sycara, "A Preliminary Report
  of a Public Experiment of a Semantic Service
  Matchmaker combined with a UDDI Business
  Registry," in 1st International Conference on
  Service Oriented Computing (ICSOC 2003), Trento,
  Italy, December 2003.

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Thank you for attention!