SlideShare a Scribd company logo
1 of 23
X-SOM A Flexible Ontology Mapper Carlo Curino,  Giorgio Orsi , Letizia Tanca {curino,orsi,tanca}@elet.polimi.it  Politecnico di Milano Dipartimento di Elettronica e Informazione September 4 th SWAE 2007 (DEXA’07)‏ Regensburg
Motivations ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Outline ,[object Object],[object Object],[object Object],[object Object]
The Problem Alignment Ontology Alignment :   The process of bringing two or more ontologies into  mutual agreement , by relating their constitutive  elements by means of  alignment relationships , and  making them  coherent and consistent. .
The Problem Matching Ontology Alignment :   The process of bringing two or more ontologies into  mutual agreement , by relating their constitutive  elements by means of  alignment relationships , and  making them  coherent and consistent. .
The Problem Mapping Ontology Alignment :   The process of bringing two or more ontologies into  mutual agreement , by relating their constitutive  elements by means of  alignment relationships , and  making them  coherent and consistent.
X-SOM’s mapping process Matching:   Similarities between ontologies computed with a customizable set of matching algorithms (strategy). The results are combined by means of a feed-forward neural network. Debugging:   Matchings are tested for consistency and coherency to improve their quality. Conflicts are solved in a (semi-)automatic fashion. Mapping:   An ontology containing the mappings between the constitutive components of the input ontologies.
X-SOM Architecture ,[object Object],[object Object],[object Object],[object Object]
Matching phase: Production ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Matching phase: Combination ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Controversial points ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Matchings debugging ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Semantic consistency: Examples ,[object Object],[object Object]
Semantic consistency: Solutions ,[object Object],[object Object]
Experimental Results: OAEI 2007
Experimental Results: OAEI 2007
Conclusion and Future Work ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Question time ,[object Object],[object Object]
 
Overall System Architecture
Models view
Data Tailoring ,[object Object],[object Object],[object Object]
Semantic Extraction ,[object Object],[object Object],[object Object],[object Object]

More Related Content

What's hot

Yu_Wang_Resume
Yu_Wang_ResumeYu_Wang_Resume
Yu_Wang_Resume
Ryan Wang
 
MultiObjective(11) - Copy
MultiObjective(11) - CopyMultiObjective(11) - Copy
MultiObjective(11) - Copy
AMIT KUMAR
 

What's hot (15)

The Advancement and Challenges in Computational Physics - Phdassistance
The Advancement and Challenges in Computational Physics - PhdassistanceThe Advancement and Challenges in Computational Physics - Phdassistance
The Advancement and Challenges in Computational Physics - Phdassistance
 
Artificial Intelligence
Artificial IntelligenceArtificial Intelligence
Artificial Intelligence
 
graph_embeddings
graph_embeddingsgraph_embeddings
graph_embeddings
 
Yu_Wang_Resume
Yu_Wang_ResumeYu_Wang_Resume
Yu_Wang_Resume
 
WILF2011 - slides
WILF2011 - slidesWILF2011 - slides
WILF2011 - slides
 
Ws2001 sessione8 cibella_tuoto
Ws2001 sessione8 cibella_tuotoWs2001 sessione8 cibella_tuoto
Ws2001 sessione8 cibella_tuoto
 
Simplified Fuzzy ARTMAP
Simplified Fuzzy ARTMAPSimplified Fuzzy ARTMAP
Simplified Fuzzy ARTMAP
 
algorithms
algorithmsalgorithms
algorithms
 
MultiObjective(11) - Copy
MultiObjective(11) - CopyMultiObjective(11) - Copy
MultiObjective(11) - Copy
 
Semantics In Digital Photos A Contenxtual Analysis
Semantics In Digital Photos A Contenxtual AnalysisSemantics In Digital Photos A Contenxtual Analysis
Semantics In Digital Photos A Contenxtual Analysis
 
Modular Multitask Reinforcement Learning with Policy Sketches
Modular Multitask Reinforcement Learning with Policy SketchesModular Multitask Reinforcement Learning with Policy Sketches
Modular Multitask Reinforcement Learning with Policy Sketches
 
A Linear-Algebraic Technique with an Application in Semantic Image Retrieval
A Linear-Algebraic Technique with an Application in Semantic Image RetrievalA Linear-Algebraic Technique with an Application in Semantic Image Retrieval
A Linear-Algebraic Technique with an Application in Semantic Image Retrieval
 
Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace
Gradient-Based Meta-Learning with Learned Layerwise Metric and SubspaceGradient-Based Meta-Learning with Learned Layerwise Metric and Subspace
Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace
 
Cyclic Neural Networks
Cyclic Neural NetworksCyclic Neural Networks
Cyclic Neural Networks
 
Computation of Neural Network using C# with Respect to Bioinformatics
Computation of Neural Network using C# with Respect to BioinformaticsComputation of Neural Network using C# with Respect to Bioinformatics
Computation of Neural Network using C# with Respect to Bioinformatics
 

Viewers also liked (9)

Hot technologies slideshare
Hot technologies   slideshareHot technologies   slideshare
Hot technologies slideshare
 
Po presentation ddc
Po presentation ddcPo presentation ddc
Po presentation ddc
 
Gottlob ICDE 2011
Gottlob ICDE 2011Gottlob ICDE 2011
Gottlob ICDE 2011
 
Essex Presentation
Essex PresentationEssex Presentation
Essex Presentation
 
Essex Presentation
Essex PresentationEssex Presentation
Essex Presentation
 
Orsi Vldb11
Orsi Vldb11Orsi Vldb11
Orsi Vldb11
 
Phdaey
PhdaeyPhdaey
Phdaey
 
Po presentation ddc
Po presentation ddcPo presentation ddc
Po presentation ddc
 
The Diadem Ontology
The Diadem OntologyThe Diadem Ontology
The Diadem Ontology
 

Similar to Dexa2007 Orsi V1.5

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
 
Ambient Intelligence in Adaptive Online Experiments
Ambient Intelligence in Adaptive Online ExperimentsAmbient Intelligence in Adaptive Online Experiments
Ambient Intelligence in Adaptive Online Experiments
Violeta Damjanovic-Behrendt
 
X Som Graduation Presentation
X Som   Graduation PresentationX Som   Graduation Presentation
X Som Graduation Presentation
Giorgio Orsi
 
abstrakty přijatých příspěvků.doc
abstrakty přijatých příspěvků.docabstrakty přijatých příspěvků.doc
abstrakty přijatých příspěvků.doc
butest
 
Predicting Fault-Prone Files using Machine Learning
Predicting Fault-Prone Files using Machine LearningPredicting Fault-Prone Files using Machine Learning
Predicting Fault-Prone Files using Machine Learning
Guido A. Ciollaro
 

Similar to Dexa2007 Orsi V1.5 (20)

Barzilay & Lapata 2008 presentation
Barzilay & Lapata 2008 presentationBarzilay & Lapata 2008 presentation
Barzilay & Lapata 2008 presentation
 
Nature Inspired Models And The Semantic Web
Nature Inspired Models And The Semantic WebNature Inspired Models And The Semantic Web
Nature Inspired Models And The Semantic Web
 
A SYSTEM OF SERIAL COMPUTATION FOR CLASSIFIED RULES PREDICTION IN NONREGULAR ...
A SYSTEM OF SERIAL COMPUTATION FOR CLASSIFIED RULES PREDICTION IN NONREGULAR ...A SYSTEM OF SERIAL COMPUTATION FOR CLASSIFIED RULES PREDICTION IN NONREGULAR ...
A SYSTEM OF SERIAL COMPUTATION FOR CLASSIFIED RULES PREDICTION IN NONREGULAR ...
 
SEMANTIC INTEGRATION FOR AUTOMATIC ONTOLOGY MAPPING
SEMANTIC INTEGRATION FOR AUTOMATIC ONTOLOGY MAPPING SEMANTIC INTEGRATION FOR AUTOMATIC ONTOLOGY MAPPING
SEMANTIC INTEGRATION FOR AUTOMATIC ONTOLOGY MAPPING
 
Ambient Intelligence in Adaptive Online Experiments
Ambient Intelligence in Adaptive Online ExperimentsAmbient Intelligence in Adaptive Online Experiments
Ambient Intelligence in Adaptive Online Experiments
 
IEEE Datamining 2016 Title and Abstract
IEEE  Datamining 2016 Title and AbstractIEEE  Datamining 2016 Title and Abstract
IEEE Datamining 2016 Title and Abstract
 
Data Integration Ontology Mapping
Data Integration Ontology MappingData Integration Ontology Mapping
Data Integration Ontology Mapping
 
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
 
X Som Graduation Presentation
X Som   Graduation PresentationX Som   Graduation Presentation
X Som Graduation Presentation
 
abstrakty přijatých příspěvků.doc
abstrakty přijatých příspěvků.docabstrakty přijatých příspěvků.doc
abstrakty přijatých příspěvků.doc
 
A COMPARISON OF DOCUMENT SIMILARITY ALGORITHMS
A COMPARISON OF DOCUMENT SIMILARITY ALGORITHMSA COMPARISON OF DOCUMENT SIMILARITY ALGORITHMS
A COMPARISON OF DOCUMENT SIMILARITY ALGORITHMS
 
A COMPARISON OF DOCUMENT SIMILARITY ALGORITHMS
A COMPARISON OF DOCUMENT SIMILARITY ALGORITHMSA COMPARISON OF DOCUMENT SIMILARITY ALGORITHMS
A COMPARISON OF DOCUMENT SIMILARITY ALGORITHMS
 
IEEE Pattern analysis and machine intelligence 2016 Title and Abstract
IEEE Pattern analysis and machine intelligence 2016 Title and AbstractIEEE Pattern analysis and machine intelligence 2016 Title and Abstract
IEEE Pattern analysis and machine intelligence 2016 Title and Abstract
 
Introduction to Machine Learning with SciKit-Learn
Introduction to Machine Learning with SciKit-LearnIntroduction to Machine Learning with SciKit-Learn
Introduction to Machine Learning with SciKit-Learn
 
Learning from similarity and information extraction from structured documents...
Learning from similarity and information extraction from structured documents...Learning from similarity and information extraction from structured documents...
Learning from similarity and information extraction from structured documents...
 
mlss
mlssmlss
mlss
 
Predicting Fault-Prone Files using Machine Learning
Predicting Fault-Prone Files using Machine LearningPredicting Fault-Prone Files using Machine Learning
Predicting Fault-Prone Files using Machine Learning
 
ANALYSIS AND COMPARISON STUDY OF DATA MINING ALGORITHMS USING RAPIDMINER
ANALYSIS AND COMPARISON STUDY OF DATA MINING ALGORITHMS USING RAPIDMINERANALYSIS AND COMPARISON STUDY OF DATA MINING ALGORITHMS USING RAPIDMINER
ANALYSIS AND COMPARISON STUDY OF DATA MINING ALGORITHMS USING RAPIDMINER
 
The Status of ML Algorithms for Structure-property Relationships Using Matb...
The Status of ML Algorithms for Structure-property Relationships Using Matb...The Status of ML Algorithms for Structure-property Relationships Using Matb...
The Status of ML Algorithms for Structure-property Relationships Using Matb...
 

More from Giorgio Orsi

wadar_poster_final
wadar_poster_finalwadar_poster_final
wadar_poster_final
Giorgio Orsi
 
ROSeAnn: Reconciling Opinions of Semantic Annotators VLDB 2014
ROSeAnn: Reconciling Opinions of Semantic Annotators VLDB 2014ROSeAnn: Reconciling Opinions of Semantic Annotators VLDB 2014
ROSeAnn: Reconciling Opinions of Semantic Annotators VLDB 2014
Giorgio Orsi
 
Datalog and its Extensions for Semantic Web Databases
Datalog and its Extensions for Semantic Web DatabasesDatalog and its Extensions for Semantic Web Databases
Datalog and its Extensions for Semantic Web Databases
Giorgio Orsi
 
AMBER WWW 2012 Poster
AMBER WWW 2012 PosterAMBER WWW 2012 Poster
AMBER WWW 2012 Poster
Giorgio Orsi
 
DIADEM WWW 2012
DIADEM WWW 2012DIADEM WWW 2012
DIADEM WWW 2012
Giorgio Orsi
 

More from Giorgio Orsi (20)

Web Data Extraction: A Crash Course
Web Data Extraction: A Crash CourseWeb Data Extraction: A Crash Course
Web Data Extraction: A Crash Course
 
Fairhair.ai – alan turing institute june '17 (public)
Fairhair.ai – alan turing institute june '17 (public)Fairhair.ai – alan turing institute june '17 (public)
Fairhair.ai – alan turing institute june '17 (public)
 
Joint Repairs for Web Wrappers
Joint Repairs for Web WrappersJoint Repairs for Web Wrappers
Joint Repairs for Web Wrappers
 
SAE: Structured Aspect Extraction
SAE: Structured Aspect ExtractionSAE: Structured Aspect Extraction
SAE: Structured Aspect Extraction
 
diadem-vldb-2015
diadem-vldb-2015diadem-vldb-2015
diadem-vldb-2015
 
wadar_poster_final
wadar_poster_finalwadar_poster_final
wadar_poster_final
 
Query Rewriting and Optimization for Ontological Databases
Query Rewriting and Optimization for Ontological DatabasesQuery Rewriting and Optimization for Ontological Databases
Query Rewriting and Optimization for Ontological Databases
 
ROSeAnn: Reconciling Opinions of Semantic Annotators VLDB 2014
ROSeAnn: Reconciling Opinions of Semantic Annotators VLDB 2014ROSeAnn: Reconciling Opinions of Semantic Annotators VLDB 2014
ROSeAnn: Reconciling Opinions of Semantic Annotators VLDB 2014
 
Deos 2014 - Welcome
Deos 2014 - WelcomeDeos 2014 - Welcome
Deos 2014 - Welcome
 
Perv a ds-rr13
Perv a ds-rr13Perv a ds-rr13
Perv a ds-rr13
 
Heuristic Ranking in Tightly Coupled Probabilistic Description Logics
Heuristic Ranking in Tightly Coupled Probabilistic Description LogicsHeuristic Ranking in Tightly Coupled Probabilistic Description Logics
Heuristic Ranking in Tightly Coupled Probabilistic Description Logics
 
Datalog and its Extensions for Semantic Web Databases
Datalog and its Extensions for Semantic Web DatabasesDatalog and its Extensions for Semantic Web Databases
Datalog and its Extensions for Semantic Web Databases
 
AMBER WWW 2012 Poster
AMBER WWW 2012 PosterAMBER WWW 2012 Poster
AMBER WWW 2012 Poster
 
AMBER WWW 2012 (Demonstration)
AMBER WWW 2012 (Demonstration)AMBER WWW 2012 (Demonstration)
AMBER WWW 2012 (Demonstration)
 
DIADEM WWW 2012
DIADEM WWW 2012DIADEM WWW 2012
DIADEM WWW 2012
 
OPAL: a passe-partout for web forms - WWW 2012 (Demonstration)
OPAL: a passe-partout for web forms - WWW 2012 (Demonstration)OPAL: a passe-partout for web forms - WWW 2012 (Demonstration)
OPAL: a passe-partout for web forms - WWW 2012 (Demonstration)
 
Querying UML Class Diagrams - FoSSaCS 2012
Querying UML Class Diagrams - FoSSaCS 2012Querying UML Class Diagrams - FoSSaCS 2012
Querying UML Class Diagrams - FoSSaCS 2012
 
OPAL: automated form understanding for the deep web - WWW 2012
OPAL: automated form understanding for the deep web - WWW 2012OPAL: automated form understanding for the deep web - WWW 2012
OPAL: automated form understanding for the deep web - WWW 2012
 
Nyaya: Semantic data markets: a flexible environment for knowledge management...
Nyaya: Semantic data markets: a flexible environment for knowledge management...Nyaya: Semantic data markets: a flexible environment for knowledge management...
Nyaya: Semantic data markets: a flexible environment for knowledge management...
 
Table Recognition
Table RecognitionTable Recognition
Table Recognition
 

Dexa2007 Orsi V1.5

  • 1. X-SOM A Flexible Ontology Mapper Carlo Curino, Giorgio Orsi , Letizia Tanca {curino,orsi,tanca}@elet.polimi.it Politecnico di Milano Dipartimento di Elettronica e Informazione September 4 th SWAE 2007 (DEXA’07)‏ Regensburg
  • 2.
  • 3.
  • 4. The Problem Alignment Ontology Alignment : The process of bringing two or more ontologies into mutual agreement , by relating their constitutive elements by means of alignment relationships , and making them coherent and consistent. .
  • 5. The Problem Matching Ontology Alignment : The process of bringing two or more ontologies into mutual agreement , by relating their constitutive elements by means of alignment relationships , and making them coherent and consistent. .
  • 6. The Problem Mapping Ontology Alignment : The process of bringing two or more ontologies into mutual agreement , by relating their constitutive elements by means of alignment relationships , and making them coherent and consistent.
  • 7. X-SOM’s mapping process Matching: Similarities between ontologies computed with a customizable set of matching algorithms (strategy). The results are combined by means of a feed-forward neural network. Debugging: Matchings are tested for consistency and coherency to improve their quality. Conflicts are solved in a (semi-)automatic fashion. Mapping: An ontology containing the mappings between the constitutive components of the input ontologies.
  • 8.
  • 9.
  • 10.
  • 11.
  • 12.
  • 13.
  • 14.
  • 17.
  • 18.
  • 19.  
  • 22.
  • 23.

Editor's Notes

  1. Ricerca di sorgenti
  2. Ricerca di sorgenti
  3. Ricerca di sorgenti