SlideShare ist ein Scribd-Unternehmen logo
1 von 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

Weitere ähnliche Inhalte

Was ist angesagt?

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...Khirulnizam Abd Rahman
 
ONTOLOGY BASED DATA ACCESS
ONTOLOGY BASED DATA ACCESSONTOLOGY BASED DATA ACCESS
ONTOLOGY BASED DATA ACCESSKishan Patel
 
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: IntroductionKent State University
 
The Standardization of Semantic Web Ontology
The Standardization of Semantic Web OntologyThe Standardization of Semantic Web Ontology
The Standardization of Semantic Web OntologyMyungjin Lee
 
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 developmentAntonio Moreno
 
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...Kent State University
 
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 RELATIONSsipij
 
4 semantic web and ontology
4 semantic web and ontology4 semantic web and ontology
4 semantic web and ontologySanthosh Kannan
 
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 modelMihika Shah
 
Ontology Engineering for Big Data
Ontology Engineering for Big DataOntology Engineering for Big Data
Ontology Engineering for Big DataKouji Kozaki
 
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 HozoKouji Kozaki
 
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 annotationShahab Mokarizadeh
 
Introduction to Ontology Concepts and Terminology
Introduction to Ontology Concepts and TerminologyIntroduction to Ontology Concepts and Terminology
Introduction to Ontology Concepts and TerminologySteven Miller
 
NE7012- SOCIAL NETWORK ANALYSIS
NE7012- SOCIAL NETWORK ANALYSISNE7012- SOCIAL NETWORK ANALYSIS
NE7012- SOCIAL NETWORK ANALYSISrathnaarul
 

Was ist angesagt? (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
 

Ähnlich wie 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
 
Barzilay & Lapata 2008 presentation
Barzilay & Lapata 2008 presentationBarzilay & Lapata 2008 presentation
Barzilay & Lapata 2008 presentationRichard Littauer
 
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 professionalgowthamnaidu0986
 
X Som Graduation Presentation
X Som   Graduation PresentationX Som   Graduation Presentation
X Som Graduation PresentationGiorgio Orsi
 
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[Zac Darcy
 
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 OntologyZac Darcy
 
Interactive Analysis of Word Vector Embeddings
Interactive Analysis of Word Vector EmbeddingsInteractive Analysis of Word Vector Embeddings
Interactive Analysis of Word Vector Embeddingsgleicher
 
A NAIVE METHOD FOR ONTOLOGY CONSTRUCTION
A NAIVE METHOD FOR ONTOLOGY CONSTRUCTIONA NAIVE METHOD FOR ONTOLOGY CONSTRUCTION
A NAIVE METHOD FOR ONTOLOGY CONSTRUCTIONijscai
 
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
 
A NAIVE METHOD FOR ONTOLOGY CONSTRUCTION
A NAIVE METHOD FOR ONTOLOGY CONSTRUCTIONA NAIVE METHOD FOR ONTOLOGY CONSTRUCTION
A NAIVE METHOD FOR ONTOLOGY CONSTRUCTIONijscai
 
Dexa2007 Orsi V1.5
Dexa2007 Orsi V1.5Dexa2007 Orsi V1.5
Dexa2007 Orsi V1.5Giorgio Orsi
 
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 Databaseijbuiiir1
 
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...dannyijwest
 
{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...Amit Sheth
 
TEXT PLAGIARISM CHECKER USING FRIENDSHIP GRAPHS
TEXT PLAGIARISM CHECKER USING FRIENDSHIP GRAPHSTEXT PLAGIARISM CHECKER USING FRIENDSHIP GRAPHS
TEXT PLAGIARISM CHECKER USING FRIENDSHIP GRAPHSijcsit
 
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 DATAijistjournal
 
Identifying the semantic relations on
Identifying the semantic relations onIdentifying the semantic relations on
Identifying the semantic relations onijistjournal
 
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 IntegrationIJwest
 
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 EmbeddingIRJET Journal
 

Ähnlich wie 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
 

Kürzlich hochgeladen

Unraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfUnraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfAlex Barbosa Coqueiro
 
A Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptxA Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptxLoriGlavin3
 
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebDev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebUiPathCommunity
 
SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024Lorenzo Miniero
 
Training state-of-the-art general text embedding
Training state-of-the-art general text embeddingTraining state-of-the-art general text embedding
Training state-of-the-art general text embeddingZilliz
 
How to write a Business Continuity Plan
How to write a Business Continuity PlanHow to write a Business Continuity Plan
How to write a Business Continuity PlanDatabarracks
 
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptxUse of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptxLoriGlavin3
 
Sample pptx for embedding into website for demo
Sample pptx for embedding into website for demoSample pptx for embedding into website for demo
Sample pptx for embedding into website for demoHarshalMandlekar2
 
DevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenDevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenHervé Boutemy
 
What is DBT - The Ultimate Data Build Tool.pdf
What is DBT - The Ultimate Data Build Tool.pdfWhat is DBT - The Ultimate Data Build Tool.pdf
What is DBT - The Ultimate Data Build Tool.pdfMounikaPolabathina
 
DevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsDevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsSergiu Bodiu
 
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxMerck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxLoriGlavin3
 
"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii SoldatenkoFwdays
 
A Journey Into the Emotions of Software Developers
A Journey Into the Emotions of Software DevelopersA Journey Into the Emotions of Software Developers
A Journey Into the Emotions of Software DevelopersNicole Novielli
 
How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.Curtis Poe
 
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024BookNet Canada
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek SchlawackFwdays
 
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptxThe Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptxLoriGlavin3
 
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptxThe Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptxLoriGlavin3
 
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024BookNet Canada
 

Kürzlich hochgeladen (20)

Unraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfUnraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdf
 
A Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptxA Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptx
 
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebDev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio Web
 
SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024
 
Training state-of-the-art general text embedding
Training state-of-the-art general text embeddingTraining state-of-the-art general text embedding
Training state-of-the-art general text embedding
 
How to write a Business Continuity Plan
How to write a Business Continuity PlanHow to write a Business Continuity Plan
How to write a Business Continuity Plan
 
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptxUse of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
 
Sample pptx for embedding into website for demo
Sample pptx for embedding into website for demoSample pptx for embedding into website for demo
Sample pptx for embedding into website for demo
 
DevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenDevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache Maven
 
What is DBT - The Ultimate Data Build Tool.pdf
What is DBT - The Ultimate Data Build Tool.pdfWhat is DBT - The Ultimate Data Build Tool.pdf
What is DBT - The Ultimate Data Build Tool.pdf
 
DevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsDevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platforms
 
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxMerck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
 
"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko
 
A Journey Into the Emotions of Software Developers
A Journey Into the Emotions of Software DevelopersA Journey Into the Emotions of Software Developers
A Journey Into the Emotions of Software Developers
 
How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.
 
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
 
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptxThe Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
 
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptxThe Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
 
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
 

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.