This document discusses ontology mapping. It begins with an introduction to the semantic web and ontologies. Ontology mapping is important for allowing different ontologies to be aligned and related. There are different types of ontology mapping including alignment, merging, and mapping. The document then surveys some popular ontology mapping techniques including GLUE, PROMPT, and QOM. It evaluates these techniques and discusses their inputs, outputs, and approaches. The document concludes that semantic web research is important for advancing web technologies and realizing the goals of web 3.0. Future work could involve developing new ontology mapping techniques and publishing research on existing mapping methods.

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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