Ontology mapping for the semantic web

1. Mr. worawith sangkatip
Student Master of Information Technology (IT)
Faculty Informatics
Mahasarakham University
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2. Agenda
 Introduction
 Semantic Web
 Ontology
 Ontology Mapping
 Ontology Mapping Research
 Survey Ontology Mapping Techniques
 Conclusion
 Future Work
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3. Introduction
 Present Web Technology and Semantic web
 Present Semantic web challenges Research
 Present Related Work Research
 Present Concept Research
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4. Semantic Web
 Technology Web present
 Web pages are writing in html tag
 Html describes the syntax not the semantic
 Search engine return keyword and link
 Computer not understand the meaning behind
information
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5. Semantic Web
 Why Semantic Web ?
 the information that the user wants
 Computer understand the meaning behind information
 the semantic web as a component of Web 3.0
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6. Semantic Web
 semantic of social connections
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Source : Radar Networks & Nova Spivack, 2007

7. Semantic Web
 Semantic Web from the defines of Tim BernnersLee
 Semantic Web is a group of methods and technologies
to allow machines to understand the meaning
 metadata is an important part the semantic web
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8. Semantic Web
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Source : The World Wide Web Consortium (W3C)
 Semantic Web stack

9. Ontology
 ontology is one component of the technology
Semantic Web.
 A concept to describe the knowledge.
 Standard for understanding the meaning and scope of
information of interest.
 Ontology is useful in the search better. With accuracy.
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10. Ontology
ontology can be used instead of semantic information to
describe.
 RDF – (Resource Description Framework)
 OWL – (Web Ontology Language) adds semantic meaning
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11. Ontology
 RDF (Resource Description Framework)
a model for expressing metadata as triples (subject-predicate-object)
Subject
ObjectPredicate
www.bicmsu.com
BICsite-owner
<? xml version= “1.0” encoding= “UTF-16” ?>
< rdf:RDF
xmlns:rdf = “http://www.w3.org/1999/02/22-rdf-syntax-ns#”
<rdf:Description rdf : about = “http://www.bic-msu.com”>
<mydomain : site-owner>
BIC (business intelligent center)
</mydomain : site-owner>
</rdf:Description>
</rdf:RDF>
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12. Ontology
 OWL (Web Ontology Language)
 OWL is a language that describes the data than the
language RDF.
 the class (class), property (properties) and relationships.
(Relationship).
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Class1
Class3Class2
Properties:
properties1: type1
properties2: type2
……
Class Hierachical
<owl :ObjectProperty rdf:ID= “teaches”>
<rdfs:range rdf:resource= #course”/>
<rdfs:domain rdf:resource= #academicStaffMember”/>
<rdfs:subPropertyOf rdf:resource= #Involves”/>
</owl: ObjectProperty >

13. Ontology Mapping
 Why Ontology Mapping ?
 organization has its own ontology.
 use the shared ontology.
 problem in terms of data Replace or concept different.
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14. Ontology Mapping
 What is Ontology Mapping Problem ?
 Specification of a conceptualization different
 specification of the terms in the domain and relations
different
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15. Ontology Mapping
 Type of Ontology Mapping
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Ontology AlignmentOntology Mapping
Ontology Merging

16. Ontology mapping
 Ontology mapping is important
 database integration,
heterogeneous database retrieval (traditional)
 catalog matching (e-commerce)
 agent communication (theory only)
 web service integration (urgent)
 P2P information sharing (emerging)
 personalisation (emerging)
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17. Ontology Mapping Research
 Semantic web Challenges [1]
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A Classification of the Semantic Web Challenges

18. Ontology Mapping Research
Research Classification of Ontology Mapping [2]
 Mapping Discovery
 Declarative formal representation of mappings
 Reasoning with mappings
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19. Survey Ontology Mapping Techniques
 GLUE
 PROMPT
 QOM
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20.  GLUE [3] Machine learning techniques to find mappings.
 GLUE architecture consists of
 Distribution Estimator
 Similarity Estimator.
 Relaxation Labeler
 GLUE output's one to one correspondences between the
taxonomies the ontologies .
 String similarity, structure and machine learning strategies.
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Survey Ontology Mapping Techniques

21.  PROMPT [4]
 Input: Two ontology's in OWL/ OKBC
 Output: Suggestions of mapping and
a merging ontology based on the choice made by the user.
 iPROMPT : Interactive ontology merging tool.
 AnchorPROMT : Graph-based mappings to
provide additional information for iPROMPT.
 PROMPTDiff : Compares different ontology
versions by combining matchers in a fixed point manner.
 PROMPTFactor : Tool for extracting a part of an ontology.
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Survey Ontology Mapping Techniques

22.  QOM [5]
String similarity, structure and instances.
Input : Two OWL or RDFS ontology's with elements (e.g., classes, properties,
instances) in the ontology's
Output: One-to-one or one-to-none correspondences.
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Survey Ontology Mapping Techniques

23. Conclusion
 Semantic web in a range of research and development.
 Semantic web research is innovative and important.
 Now in the era of web 3.0.
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24. Future Work
 Research Ontology Mapping New Techniques
 Publish Research of survey ontology mapping
techniques
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25. References
 [1] Keyvanpour M, Hassanzadeh H, Mohammadizadeh B. "Comparative
Classification of Semantic Web Challenges and Data Mining Techniques". Web
Information Systems and Mining, 2009. Shanghai 2009. 200-203.
 [2] - N. Noy. Semantic Integration: A Survey of Ontology-based Approaches. Sigmond
Record, Special Issue on Semantic Integration. 2004
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26. Questions?
Thank You.
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