SlideShare uma empresa Scribd logo
1 de 27
Pradeep Pillai and Michael Kandefer Department of Computer Science and Engineering University at Buffalo  Buffalo, NY, 14260 {pbpillai,mwk3}@cse.buffalo.edu Schema Matching and Ontology Mapping: A Comparison
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Introduction
[object Object],[object Object],[object Object],[object Object],[object Object],AGENDA
[object Object],[object Object],[object Object],[object Object],Schema Matching Definition
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Schema matching topology
[object Object],[object Object],[object Object],[object Object],[object Object],Element-based: String mappings
Element-based: String mappings - Prefix(3) - 3-Gram(2) - Edit distance(5) PurchaseOrder DeliverTo InvoiceTo Items Address Address Item Street City City Street ItemCount ItemNumber Quantity UnitOfMeasure PO POShipTo POBillTo POLines Item Street City City Street Count Line Qty UoM
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Element-based: Language mappings
Element-based: Language mappings POBillTo InvoiceTo <PO,Bill,To> <Invoice,To> Tokenize: <PO,Bill> <Invoice> Elimination: Expansion: <Purchase,Order,Bill> <Invoice> <Purchase,Order,Bill> <Bill> Related form: PurchaseOrder DeliverTo InvoiceTo Items Address Address Item Street City City Street ItemCount ItemNumber Quantity UnitOfMeasure PO POShipTo POBillTo POLines Item Street City City Street Count Line Qty UoM
[object Object],[object Object],[object Object],[object Object],[object Object],Structure-based: Graph/tree mappings
[object Object],[object Object],[object Object],[object Object],[object Object],Ontology Mapping Problem
[object Object],[object Object],[object Object],[object Object],Ontology Mapping Research
[object Object],[object Object],[object Object],[object Object],Survey : State of the Art
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],GLUE
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],PROMPT
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],IR Approaches
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],QOM
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Schemas and Ontologies
[object Object],[object Object],[object Object],Similarities
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Differences
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Consequences of Differences
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Schema -> Ontology
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Schema Matching Algorithm
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Tree Matcher
Cupid Mappings - High  lsim (A1) - High  wsim (A2) - Matches PurchaseOrder DeliverTo InvoiceTo Items Address Address Item Street City City Street ItemCount ItemNumber Quantity UnitOfMeasure PO POShipTo POBillTo POLines Item Street City City Street Count Line Qty UoM
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Conclusions
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],References

Mais conteúdo relacionado

Mais procurados

Ontology-based Classification and Faceted Search Interface for APIs
Ontology-based Classification and Faceted Search Interface for APIsOntology-based Classification and Faceted Search Interface for APIs
Ontology-based Classification and Faceted Search Interface for APIs
New York City College of Technology Computer Systems Technology Colloquium
 
Representation of ontology by Classified Interrelated object model
Representation of ontology by Classified Interrelated object modelRepresentation of ontology by Classified Interrelated object model
Representation of ontology by Classified Interrelated object model
Mihika Shah
 
Ekaw ontology learning for cost effective large-scale semantic annotation
Ekaw ontology learning for cost effective large-scale semantic annotationEkaw ontology learning for cost effective large-scale semantic annotation
Ekaw ontology learning for cost effective large-scale semantic annotation
Shahab Mokarizadeh
 

Mais procurados (20)

Application of Ontology in Semantic Information Retrieval by Prof Shahrul Azm...
Application of Ontology in Semantic Information Retrieval by Prof Shahrul Azm...Application of Ontology in Semantic Information Retrieval by Prof Shahrul Azm...
Application of Ontology in Semantic Information Retrieval by Prof Shahrul Azm...
 
Ontology
OntologyOntology
Ontology
 
ONTOLOGY BASED DATA ACCESS
ONTOLOGY BASED DATA ACCESSONTOLOGY BASED DATA ACCESS
ONTOLOGY BASED DATA ACCESS
 
Ontology
Ontology Ontology
Ontology
 
Semantic Web, Ontology, and Ontology Learning: Introduction
Semantic Web, Ontology, and Ontology Learning: IntroductionSemantic Web, Ontology, and Ontology Learning: Introduction
Semantic Web, Ontology, and Ontology Learning: Introduction
 
The Standardization of Semantic Web Ontology
The Standardization of Semantic Web OntologyThe Standardization of Semantic Web Ontology
The Standardization of Semantic Web Ontology
 
Lect6-An introduction to ontologies and ontology development
Lect6-An introduction to ontologies and ontology developmentLect6-An introduction to ontologies and ontology development
Lect6-An introduction to ontologies and ontology development
 
Using Text Comprehension Model for Learning Concepts, Context, and Topic of...
Using Text Comprehension Model for  Learning Concepts, Context, and Topic  of...Using Text Comprehension Model for  Learning Concepts, Context, and Topic  of...
Using Text Comprehension Model for Learning Concepts, Context, and Topic of...
 
ONTOLOGICAL MODEL FOR CHARACTER RECOGNITION BASED ON SPATIAL RELATIONS
ONTOLOGICAL MODEL FOR CHARACTER RECOGNITION BASED ON SPATIAL RELATIONSONTOLOGICAL MODEL FOR CHARACTER RECOGNITION BASED ON SPATIAL RELATIONS
ONTOLOGICAL MODEL FOR CHARACTER RECOGNITION BASED ON SPATIAL RELATIONS
 
Ijetcas14 624
Ijetcas14 624Ijetcas14 624
Ijetcas14 624
 
Ijetcas14 639
Ijetcas14 639Ijetcas14 639
Ijetcas14 639
 
4 semantic web and ontology
4 semantic web and ontology4 semantic web and ontology
4 semantic web and ontology
 
Ontology-based Classification and Faceted Search Interface for APIs
Ontology-based Classification and Faceted Search Interface for APIsOntology-based Classification and Faceted Search Interface for APIs
Ontology-based Classification and Faceted Search Interface for APIs
 
Representation of ontology by Classified Interrelated object model
Representation of ontology by Classified Interrelated object modelRepresentation of ontology by Classified Interrelated object model
Representation of ontology by Classified Interrelated object model
 
Ontology Engineering for Big Data
Ontology Engineering for Big DataOntology Engineering for Big Data
Ontology Engineering for Big Data
 
Ontology Building and its Application using Hozo
Ontology Building and its Application using HozoOntology Building and its Application using Hozo
Ontology Building and its Application using Hozo
 
Ontology
OntologyOntology
Ontology
 
Ekaw ontology learning for cost effective large-scale semantic annotation
Ekaw ontology learning for cost effective large-scale semantic annotationEkaw ontology learning for cost effective large-scale semantic annotation
Ekaw ontology learning for cost effective large-scale semantic annotation
 
Introduction to Ontology Concepts and Terminology
Introduction to Ontology Concepts and TerminologyIntroduction to Ontology Concepts and Terminology
Introduction to Ontology Concepts and Terminology
 
NE7012- SOCIAL NETWORK ANALYSIS
NE7012- SOCIAL NETWORK ANALYSISNE7012- SOCIAL NETWORK ANALYSIS
NE7012- SOCIAL NETWORK ANALYSIS
 

Semelhante a Data Integration Ontology Mapping

SEMANTIC INTEGRATION FOR AUTOMATIC ONTOLOGY MAPPING
SEMANTIC INTEGRATION FOR AUTOMATIC ONTOLOGY MAPPING SEMANTIC INTEGRATION FOR AUTOMATIC ONTOLOGY MAPPING
SEMANTIC INTEGRATION FOR AUTOMATIC ONTOLOGY MAPPING
cscpconf
 
X Som Graduation Presentation
X Som   Graduation PresentationX Som   Graduation Presentation
X Som Graduation Presentation
Giorgio Orsi
 
A Naive Method For Ontology Construction
A Naive Method For Ontology Construction A Naive Method For Ontology Construction
A Naive Method For Ontology Construction
IJSCAI Journal
 
Dexa2007 Orsi V1.5
Dexa2007 Orsi V1.5Dexa2007 Orsi V1.5
Dexa2007 Orsi V1.5
Giorgio Orsi
 

Semelhante a Data Integration Ontology Mapping (20)

SEMANTIC INTEGRATION FOR AUTOMATIC ONTOLOGY MAPPING
SEMANTIC INTEGRATION FOR AUTOMATIC ONTOLOGY MAPPING SEMANTIC INTEGRATION FOR AUTOMATIC ONTOLOGY MAPPING
SEMANTIC INTEGRATION FOR AUTOMATIC ONTOLOGY MAPPING
 
Barzilay & Lapata 2008 presentation
Barzilay & Lapata 2008 presentationBarzilay & Lapata 2008 presentation
Barzilay & Lapata 2008 presentation
 
SWSN UNIT-3.pptx we can information about swsn professional
SWSN UNIT-3.pptx we can information about swsn professionalSWSN UNIT-3.pptx we can information about swsn professional
SWSN UNIT-3.pptx we can information about swsn professional
 
X Som Graduation Presentation
X Som   Graduation PresentationX Som   Graduation Presentation
X Som Graduation Presentation
 
Conceptual similarity measurement algorithm for domain specific ontology[
Conceptual similarity measurement algorithm for domain specific ontology[Conceptual similarity measurement algorithm for domain specific ontology[
Conceptual similarity measurement algorithm for domain specific ontology[
 
Conceptual Similarity Measurement Algorithm For Domain Specific Ontology
Conceptual Similarity Measurement Algorithm For Domain Specific OntologyConceptual Similarity Measurement Algorithm For Domain Specific Ontology
Conceptual Similarity Measurement Algorithm For Domain Specific Ontology
 
Interactive Analysis of Word Vector Embeddings
Interactive Analysis of Word Vector EmbeddingsInteractive Analysis of Word Vector Embeddings
Interactive Analysis of Word Vector Embeddings
 
A NAIVE METHOD FOR ONTOLOGY CONSTRUCTION
A NAIVE METHOD FOR ONTOLOGY CONSTRUCTIONA NAIVE METHOD FOR ONTOLOGY CONSTRUCTION
A NAIVE METHOD FOR ONTOLOGY CONSTRUCTION
 
A Naive Method For Ontology Construction
A Naive Method For Ontology Construction A Naive Method For Ontology Construction
A Naive Method For Ontology Construction
 
A NAIVE METHOD FOR ONTOLOGY CONSTRUCTION
A NAIVE METHOD FOR ONTOLOGY CONSTRUCTIONA NAIVE METHOD FOR ONTOLOGY CONSTRUCTION
A NAIVE METHOD FOR ONTOLOGY CONSTRUCTION
 
Dexa2007 Orsi V1.5
Dexa2007 Orsi V1.5Dexa2007 Orsi V1.5
Dexa2007 Orsi V1.5
 
Towards Ontology Development Based on Relational Database
Towards Ontology Development Based on Relational DatabaseTowards Ontology Development Based on Relational Database
Towards Ontology Development Based on Relational Database
 
L1803058388
L1803058388L1803058388
L1803058388
 
Ontology Matching Based on hypernym, hyponym, holonym, and meronym Sets in Wo...
Ontology Matching Based on hypernym, hyponym, holonym, and meronym Sets in Wo...Ontology Matching Based on hypernym, hyponym, holonym, and meronym Sets in Wo...
Ontology Matching Based on hypernym, hyponym, holonym, and meronym Sets in Wo...
 
{Ontology: Resource} x {Matching : Mapping} x {Schema : Instance} :: Compone...
{Ontology: Resource} x {Matching : Mapping} x {Schema : Instance} :: Compone...{Ontology: Resource} x {Matching : Mapping} x {Schema : Instance} :: Compone...
{Ontology: Resource} x {Matching : Mapping} x {Schema : Instance} :: Compone...
 
TEXT PLAGIARISM CHECKER USING FRIENDSHIP GRAPHS
TEXT PLAGIARISM CHECKER USING FRIENDSHIP GRAPHSTEXT PLAGIARISM CHECKER USING FRIENDSHIP GRAPHS
TEXT PLAGIARISM CHECKER USING FRIENDSHIP GRAPHS
 
IDENTIFYING THE SEMANTIC RELATIONS ON UNSTRUCTURED DATA
IDENTIFYING THE SEMANTIC RELATIONS ON UNSTRUCTURED DATAIDENTIFYING THE SEMANTIC RELATIONS ON UNSTRUCTURED DATA
IDENTIFYING THE SEMANTIC RELATIONS ON UNSTRUCTURED DATA
 
Identifying the semantic relations on
Identifying the semantic relations onIdentifying the semantic relations on
Identifying the semantic relations on
 
Image-Based Literal Node Matching for Linked Data Integration
Image-Based Literal Node Matching for Linked Data IntegrationImage-Based Literal Node Matching for Linked Data Integration
Image-Based Literal Node Matching for Linked Data Integration
 
IRJET- Short-Text Semantic Similarity using Glove Word Embedding
IRJET- Short-Text Semantic Similarity using Glove Word EmbeddingIRJET- Short-Text Semantic Similarity using Glove Word Embedding
IRJET- Short-Text Semantic Similarity using Glove Word Embedding
 

Último

Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
panagenda
 
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
?#DUbAI#??##{{(☎️+971_581248768%)**%*]'#abortion pills for sale in dubai@
 

Último (20)

Corporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptxCorporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptx
 
Artificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : UncertaintyArtificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : Uncertainty
 
DBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor PresentationDBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor Presentation
 
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfBoost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
 
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
 
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
 
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
 
Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
 
FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024
 
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot TakeoffStrategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
 
Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...
 
Exploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with MilvusExploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with Milvus
 
Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)
 
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
 
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, AdobeApidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
 
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ..."I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
WSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering DevelopersWSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering Developers
 
Understanding the FAA Part 107 License ..
Understanding the FAA Part 107 License ..Understanding the FAA Part 107 License ..
Understanding the FAA Part 107 License ..
 
Mcleodganj Call Girls 🥰 8617370543 Service Offer VIP Hot Model
Mcleodganj Call Girls 🥰 8617370543 Service Offer VIP Hot ModelMcleodganj Call Girls 🥰 8617370543 Service Offer VIP Hot Model
Mcleodganj Call Girls 🥰 8617370543 Service Offer VIP Hot Model
 

Data Integration Ontology Mapping

  • 1. Pradeep Pillai and Michael Kandefer Department of Computer Science and Engineering University at Buffalo Buffalo, NY, 14260 {pbpillai,mwk3}@cse.buffalo.edu Schema Matching and Ontology Mapping: A Comparison
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7. Element-based: String mappings - Prefix(3) - 3-Gram(2) - Edit distance(5) PurchaseOrder DeliverTo InvoiceTo Items Address Address Item Street City City Street ItemCount ItemNumber Quantity UnitOfMeasure PO POShipTo POBillTo POLines Item Street City City Street Count Line Qty UoM
  • 8.
  • 9. Element-based: Language mappings POBillTo InvoiceTo <PO,Bill,To> <Invoice,To> Tokenize: <PO,Bill> <Invoice> Elimination: Expansion: <Purchase,Order,Bill> <Invoice> <Purchase,Order,Bill> <Bill> Related form: PurchaseOrder DeliverTo InvoiceTo Items Address Address Item Street City City Street ItemCount ItemNumber Quantity UnitOfMeasure PO POShipTo POBillTo POLines Item Street City City Street Count Line Qty UoM
  • 10.
  • 11.
  • 12.
  • 13.
  • 14.
  • 15.
  • 16.
  • 17.
  • 18.
  • 19.
  • 20.
  • 21.
  • 22.
  • 23.
  • 24.
  • 25. Cupid Mappings - High lsim (A1) - High wsim (A2) - Matches PurchaseOrder DeliverTo InvoiceTo Items Address Address Item Street City City Street ItemCount ItemNumber Quantity UnitOfMeasure PO POShipTo POBillTo POLines Item Street City City Street Count Line Qty UoM
  • 26.
  • 27.