SlideShare a Scribd company logo
1 of 31
Query Translation for Ontology-extended Data Sources Jie Bao 1 , Doina Caragea 2 , Vasant Honavar 1 1 Artificial Intelligence Research Laboratory, Department of Computer Science, Iowa State University, Ames, IA 50011-1040, USA {baojie, honavar}@cs.iastate.edu 2 Department of Computing and Information Sciences Kansas State University, Manhattan, KS 66506, USA {dcaragea}@ksu.edu
INDUS Group Vasant Honavar Jie Bao Doina Caragea Jyotishman Pathak Neeraj Koul
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Background ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Solution: INDUS for Learning from Semantically Heterogeneous Distributed Autonomous Data Sources
(Relational) Data Source D Data Set Extensional Definition (Facts) MSc Bob First-year Alice status name Student algorithm CS511 data structure CS103 name code Classes CS511 Bob CS103 Alice class instructor Registers S Schema Intensional Definition Classes Faculty Teaches name:String code:String rank:String name:String Student Registers name:String status:String
Semantic Extensions of Data Sources Return classes that  graduate  students are registered in Return all  people   in the database ? ? D S MSc Bob First-year Alice status name Student algorithm CS511 data structure CS103 name code Classes CS511 Bob CS103 Alice class instructor Registers
Ontology-Extended Data Source Classes Instructor Teaches name:String code:String rank:String name:String Student registers name:String status:String People Student Instructor MSc Bob First-year Alice status name Student student Undergrad Graduate First- year MSc Fourth- year … PhD MA
Ontology-Extended Data Source D Data Set S Schema O S Schema Ontology O D Data Content Ontology O’ S O’ D
Ontology-Extended Data Source ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
OEDS: Example S:  Instructor(x,y); Classes(x,y), Student(x,y)… see survey [Shvaiko & Euzenat 2005] MSc Bob First-year Alice status name Student D Classes Instructor Teaches name:String code:String rank:String name:String Student registers name:String status:String L OS  x,y, Student(x,y)    Instructor(x,y)    People(x) isa(x,y)    isa(y,z)   isa(x,z) L OD D OD isa(First-year,Undergraduate) isa(Undergraduate,Student) isa(MSc,Graduate) … O D
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Query ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Query Translation D S O 2 q’ D S q O 1 User Ontology Data Source Ontology M Ontology Mapping
Ontology Mapping ,[object Object],[object Object],[object Object],Student Undergrad Graduate First- year MSc Fourth- year … PhD MA Student Undergrad Postgraduate  Freshman … Doctoral Master into onto equ isa 1 isa 2
Query Translation Student(t) ^ isa 1 (t:status,Master) Student(t) ^ isa 2 (t.status, Graduate) Student(t) ^ isa 2 (t.status, MSc) D S O 2 q’ q D S O 1 M
Soundness, Completeness and Exactness {q} {q’} {q’} {q} {q}={q’} Sound Translation Complete Translation Exact Translation q := Student(t) ^ isa 1 (t:status,Master) q’ := Student(t) ^ isa 2 (t.status, MSc) q’ := Student(t) ^ isa 2 (t.status, Graduate) Non-existent
Most Informative Translation c 1 d 1 d 2 O 1 O 2 q := isa 1 (x,c 1 ) isa 2 (x,d 1 )    isa 2 (x,d 2 ) Most informative  sound translation! onto onto LUB (least upper bound) isa 2 (x,d 1 ) isa 2 (x,d 2 ) find its sound translation(s)
Query Translation Rules ,[object Object],(similarly for  complete  translation of complex queries) Atomic conditions Complex conditions  GLB=greatest lower bound, LUB=least upper bound
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
INDUS Tools ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
INDUS – Mapping Editor http://sourceforge.net/projects/indus-project/
INDUS – Data Editor http://sourceforge.net/projects/indus-project/
INDUS – Query Editor http://sourceforge.net/projects/indus-project/
Optimization for Scalability ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Performance ,[object Object],[object Object],[object Object],Server Client D Internet
Performance
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Conclusion ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object]
Semantics Preserving Translation ,[object Object]

More Related Content

What's hot

Database-to-Ontology Mapping Generation for Semantic Interoperability
Database-to-Ontology Mapping Generation for Semantic InteroperabilityDatabase-to-Ontology Mapping Generation for Semantic Interoperability
Database-to-Ontology Mapping Generation for Semantic InteroperabilityRaji Ghawi
 
Ir 1 lec 7
Ir 1 lec 7Ir 1 lec 7
Ir 1 lec 7alaa223
 
20181106 survey on challenges of question answering in the semantic web saltlux
20181106 survey on challenges of question answering in the semantic web saltlux20181106 survey on challenges of question answering in the semantic web saltlux
20181106 survey on challenges of question answering in the semantic web saltluxDongGyun Hong
 
The vector space model
The vector space modelThe vector space model
The vector space modelpkgosh
 
Improving Document Clustering by Eliminating Unnatural Language
Improving Document Clustering by Eliminating Unnatural LanguageImproving Document Clustering by Eliminating Unnatural Language
Improving Document Clustering by Eliminating Unnatural LanguageJinho Choi
 
Gleaning Types for Literals in RDF with Application to Entity Summarization
Gleaning Types for Literals in RDF with Application to Entity SummarizationGleaning Types for Literals in RDF with Application to Entity Summarization
Gleaning Types for Literals in RDF with Application to Entity SummarizationKalpa Gunaratna
 
Question Answering with Lydia
Question Answering with LydiaQuestion Answering with Lydia
Question Answering with LydiaJae Hong Kil
 
The Standardization of Semantic Web Ontology
The Standardization of Semantic Web OntologyThe Standardization of Semantic Web Ontology
The Standardization of Semantic Web OntologyMyungjin Lee
 
Cross language information retrieval (clir)slide
Cross language information retrieval (clir)slideCross language information retrieval (clir)slide
Cross language information retrieval (clir)slideMohd Iqbal Al-farabi
 
Lecture: Question Answering
Lecture: Question AnsweringLecture: Question Answering
Lecture: Question AnsweringMarina Santini
 
Intelligent Methods in Models of Text Information Retrieval: Implications for...
Intelligent Methods in Models of Text Information Retrieval: Implications for...Intelligent Methods in Models of Text Information Retrieval: Implications for...
Intelligent Methods in Models of Text Information Retrieval: Implications for...inscit2006
 
Semantics2018 Zhang,Petrak,Maynard: Adapted TextRank for Term Extraction: A G...
Semantics2018 Zhang,Petrak,Maynard: Adapted TextRank for Term Extraction: A G...Semantics2018 Zhang,Petrak,Maynard: Adapted TextRank for Term Extraction: A G...
Semantics2018 Zhang,Petrak,Maynard: Adapted TextRank for Term Extraction: A G...Johann Petrak
 
Ontology mapping for the semantic web
Ontology mapping for the semantic webOntology mapping for the semantic web
Ontology mapping for the semantic webWorawith Sangkatip
 

What's hot (20)

Semantic Web in Action
Semantic Web in ActionSemantic Web in Action
Semantic Web in Action
 
Database-to-Ontology Mapping Generation for Semantic Interoperability
Database-to-Ontology Mapping Generation for Semantic InteroperabilityDatabase-to-Ontology Mapping Generation for Semantic Interoperability
Database-to-Ontology Mapping Generation for Semantic Interoperability
 
Harvester_presentaion
Harvester_presentaionHarvester_presentaion
Harvester_presentaion
 
Ir 1 lec 7
Ir 1 lec 7Ir 1 lec 7
Ir 1 lec 7
 
Question answering
Question answeringQuestion answering
Question answering
 
Semantics reloaded
Semantics reloadedSemantics reloaded
Semantics reloaded
 
Chapter1 Introduction to OOP (Java)
Chapter1 Introduction to OOP (Java)Chapter1 Introduction to OOP (Java)
Chapter1 Introduction to OOP (Java)
 
20181106 survey on challenges of question answering in the semantic web saltlux
20181106 survey on challenges of question answering in the semantic web saltlux20181106 survey on challenges of question answering in the semantic web saltlux
20181106 survey on challenges of question answering in the semantic web saltlux
 
The vector space model
The vector space modelThe vector space model
The vector space model
 
Arabic question answering ‫‬
Arabic question answering ‫‬Arabic question answering ‫‬
Arabic question answering ‫‬
 
Improving Document Clustering by Eliminating Unnatural Language
Improving Document Clustering by Eliminating Unnatural LanguageImproving Document Clustering by Eliminating Unnatural Language
Improving Document Clustering by Eliminating Unnatural Language
 
Gleaning Types for Literals in RDF with Application to Entity Summarization
Gleaning Types for Literals in RDF with Application to Entity SummarizationGleaning Types for Literals in RDF with Application to Entity Summarization
Gleaning Types for Literals in RDF with Application to Entity Summarization
 
Question Answering with Lydia
Question Answering with LydiaQuestion Answering with Lydia
Question Answering with Lydia
 
07 04-06
07 04-0607 04-06
07 04-06
 
The Standardization of Semantic Web Ontology
The Standardization of Semantic Web OntologyThe Standardization of Semantic Web Ontology
The Standardization of Semantic Web Ontology
 
Cross language information retrieval (clir)slide
Cross language information retrieval (clir)slideCross language information retrieval (clir)slide
Cross language information retrieval (clir)slide
 
Lecture: Question Answering
Lecture: Question AnsweringLecture: Question Answering
Lecture: Question Answering
 
Intelligent Methods in Models of Text Information Retrieval: Implications for...
Intelligent Methods in Models of Text Information Retrieval: Implications for...Intelligent Methods in Models of Text Information Retrieval: Implications for...
Intelligent Methods in Models of Text Information Retrieval: Implications for...
 
Semantics2018 Zhang,Petrak,Maynard: Adapted TextRank for Term Extraction: A G...
Semantics2018 Zhang,Petrak,Maynard: Adapted TextRank for Term Extraction: A G...Semantics2018 Zhang,Petrak,Maynard: Adapted TextRank for Term Extraction: A G...
Semantics2018 Zhang,Petrak,Maynard: Adapted TextRank for Term Extraction: A G...
 
Ontology mapping for the semantic web
Ontology mapping for the semantic webOntology mapping for the semantic web
Ontology mapping for the semantic web
 

Viewers also liked

Ontology Mapping for Dynamic Multiagent Environment
Ontology Mapping for Dynamic Multiagent Environment Ontology Mapping for Dynamic Multiagent Environment
Ontology Mapping for Dynamic Multiagent Environment IJORCS
 
Ontology Mapping - Out Of The Babel Tower
Ontology Mapping - Out Of The Babel TowerOntology Mapping - Out Of The Babel Tower
Ontology Mapping - Out Of The Babel TowerFrank van Harmelen
 
Information Flow based Ontology Mapping - 2002
Information Flow based Ontology Mapping - 2002Information Flow based Ontology Mapping - 2002
Information Flow based Ontology Mapping - 2002Yannis Kalfoglou
 
Digital Preservation
Digital PreservationDigital Preservation
Digital PreservationSmita Chandra
 
Dp Geosc Info Presentation Final Version 2
Dp Geosc Info Presentation Final Version 2Dp Geosc Info Presentation Final Version 2
Dp Geosc Info Presentation Final Version 2Smita Chandra
 
Geoscience Australia National Map
Geoscience Australia National MapGeoscience Australia National Map
Geoscience Australia National Mapshare_s
 
Web content management
Web content managementWeb content management
Web content managementSmita Chandra
 
Institutional repositories
Institutional repositoriesInstitutional repositories
Institutional repositoriesSmita Chandra
 
How to Establish a Strong Visual Brand on Social Media
How to Establish a Strong Visual Brand on Social MediaHow to Establish a Strong Visual Brand on Social Media
How to Establish a Strong Visual Brand on Social MediaRebekah Radice
 

Viewers also liked (10)

Ontology Mapping for Dynamic Multiagent Environment
Ontology Mapping for Dynamic Multiagent Environment Ontology Mapping for Dynamic Multiagent Environment
Ontology Mapping for Dynamic Multiagent Environment
 
Ontology Mapping - Out Of The Babel Tower
Ontology Mapping - Out Of The Babel TowerOntology Mapping - Out Of The Babel Tower
Ontology Mapping - Out Of The Babel Tower
 
Information Flow based Ontology Mapping - 2002
Information Flow based Ontology Mapping - 2002Information Flow based Ontology Mapping - 2002
Information Flow based Ontology Mapping - 2002
 
Digital Preservation
Digital PreservationDigital Preservation
Digital Preservation
 
Dp Geosc Info Presentation Final Version 2
Dp Geosc Info Presentation Final Version 2Dp Geosc Info Presentation Final Version 2
Dp Geosc Info Presentation Final Version 2
 
ICoAsl
ICoAslICoAsl
ICoAsl
 
Geoscience Australia National Map
Geoscience Australia National MapGeoscience Australia National Map
Geoscience Australia National Map
 
Web content management
Web content managementWeb content management
Web content management
 
Institutional repositories
Institutional repositoriesInstitutional repositories
Institutional repositories
 
How to Establish a Strong Visual Brand on Social Media
How to Establish a Strong Visual Brand on Social MediaHow to Establish a Strong Visual Brand on Social Media
How to Establish a Strong Visual Brand on Social Media
 

Similar to Query Translation for Ontology-extended Data Sources

Fosdem 2013 petra selmer flexible querying of graph data
Fosdem 2013 petra selmer   flexible querying of graph dataFosdem 2013 petra selmer   flexible querying of graph data
Fosdem 2013 petra selmer flexible querying of graph dataPetra Selmer
 
Slides
SlidesSlides
Slidesbutest
 
Tools for Integrating Heterogeneous Data Sources from a User Perspective
Tools for Integrating Heterogeneous Data Sources from a User PerspectiveTools for Integrating Heterogeneous Data Sources from a User Perspective
Tools for Integrating Heterogeneous Data Sources from a User PerspectiveJie Bao
 
Information Integration and Knowledge Acquisition from Semantically Heterogen...
Information Integration and Knowledge Acquisition from Semantically Heterogen...Information Integration and Knowledge Acquisition from Semantically Heterogen...
Information Integration and Knowledge Acquisition from Semantically Heterogen...Jie Bao
 
INDUS: A System for Information Integration and Knowledge Acquisition from Au...
INDUS: A System for Information Integration and Knowledge Acquisition from Au...INDUS: A System for Information Integration and Knowledge Acquisition from Au...
INDUS: A System for Information Integration and Knowledge Acquisition from Au...Jie Bao
 
Language Technology Enhanced Learning
Language Technology Enhanced LearningLanguage Technology Enhanced Learning
Language Technology Enhanced Learningtelss09
 
Ontology-based Cooperation of Information Systems
Ontology-based Cooperation of Information SystemsOntology-based Cooperation of Information Systems
Ontology-based Cooperation of Information SystemsRaji Ghawi
 
Teaching & Learning with Technology TLT 2016
Teaching & Learning with Technology TLT 2016Teaching & Learning with Technology TLT 2016
Teaching & Learning with Technology TLT 2016Roy Clariana
 
Dedalo, looking for Cluster Explanations in a labyrinth of Linked Data
Dedalo, looking for Cluster Explanations in a labyrinth of Linked DataDedalo, looking for Cluster Explanations in a labyrinth of Linked Data
Dedalo, looking for Cluster Explanations in a labyrinth of Linked DataVrije Universiteit Amsterdam
 
kantorNSF-NIJ-ISI-03-06-04.ppt
kantorNSF-NIJ-ISI-03-06-04.pptkantorNSF-NIJ-ISI-03-06-04.ppt
kantorNSF-NIJ-ISI-03-06-04.pptbutest
 
Modelling and Querying Lists in RDF. A Pragmatic Study
Modelling and Querying Lists in RDF. A Pragmatic StudyModelling and Querying Lists in RDF. A Pragmatic Study
Modelling and Querying Lists in RDF. A Pragmatic StudyAlbert Meroño-Peñuela
 
Neural Text Embeddings for Information Retrieval (WSDM 2017)
Neural Text Embeddings for Information Retrieval (WSDM 2017)Neural Text Embeddings for Information Retrieval (WSDM 2017)
Neural Text Embeddings for Information Retrieval (WSDM 2017)Bhaskar Mitra
 
Natural Language Processing in R (rNLP)
Natural Language Processing in R (rNLP)Natural Language Processing in R (rNLP)
Natural Language Processing in R (rNLP)fridolin.wild
 
SSSW 2013 - Feeding Recommender Systems with Linked Open Data
SSSW 2013 - Feeding Recommender Systems with Linked Open DataSSSW 2013 - Feeding Recommender Systems with Linked Open Data
SSSW 2013 - Feeding Recommender Systems with Linked Open DataPolytechnic University of Bari
 
Stanford'12 Intro to Ontology Based Data Access for RDBMS through query rewri...
Stanford'12 Intro to Ontology Based Data Access for RDBMS through query rewri...Stanford'12 Intro to Ontology Based Data Access for RDBMS through query rewri...
Stanford'12 Intro to Ontology Based Data Access for RDBMS through query rewri...Mariano Rodriguez-Muro
 
Open nlp presentationss
Open nlp presentationssOpen nlp presentationss
Open nlp presentationssChandan Deb
 

Similar to Query Translation for Ontology-extended Data Sources (20)

Fosdem 2013 petra selmer flexible querying of graph data
Fosdem 2013 petra selmer   flexible querying of graph dataFosdem 2013 petra selmer   flexible querying of graph data
Fosdem 2013 petra selmer flexible querying of graph data
 
Slides
SlidesSlides
Slides
 
Tools for Integrating Heterogeneous Data Sources from a User Perspective
Tools for Integrating Heterogeneous Data Sources from a User PerspectiveTools for Integrating Heterogeneous Data Sources from a User Perspective
Tools for Integrating Heterogeneous Data Sources from a User Perspective
 
Information Integration and Knowledge Acquisition from Semantically Heterogen...
Information Integration and Knowledge Acquisition from Semantically Heterogen...Information Integration and Knowledge Acquisition from Semantically Heterogen...
Information Integration and Knowledge Acquisition from Semantically Heterogen...
 
INDUS: A System for Information Integration and Knowledge Acquisition from Au...
INDUS: A System for Information Integration and Knowledge Acquisition from Au...INDUS: A System for Information Integration and Knowledge Acquisition from Au...
INDUS: A System for Information Integration and Knowledge Acquisition from Au...
 
Language Technology Enhanced Learning
Language Technology Enhanced LearningLanguage Technology Enhanced Learning
Language Technology Enhanced Learning
 
Ontology-based Cooperation of Information Systems
Ontology-based Cooperation of Information SystemsOntology-based Cooperation of Information Systems
Ontology-based Cooperation of Information Systems
 
Teaching & Learning with Technology TLT 2016
Teaching & Learning with Technology TLT 2016Teaching & Learning with Technology TLT 2016
Teaching & Learning with Technology TLT 2016
 
Dedalo, looking for Cluster Explanations in a labyrinth of Linked Data
Dedalo, looking for Cluster Explanations in a labyrinth of Linked DataDedalo, looking for Cluster Explanations in a labyrinth of Linked Data
Dedalo, looking for Cluster Explanations in a labyrinth of Linked Data
 
wdb-01.ppt
wdb-01.pptwdb-01.ppt
wdb-01.ppt
 
kantorNSF-NIJ-ISI-03-06-04.ppt
kantorNSF-NIJ-ISI-03-06-04.pptkantorNSF-NIJ-ISI-03-06-04.ppt
kantorNSF-NIJ-ISI-03-06-04.ppt
 
Modelling and Querying Lists in RDF. A Pragmatic Study
Modelling and Querying Lists in RDF. A Pragmatic StudyModelling and Querying Lists in RDF. A Pragmatic Study
Modelling and Querying Lists in RDF. A Pragmatic Study
 
Neural Text Embeddings for Information Retrieval (WSDM 2017)
Neural Text Embeddings for Information Retrieval (WSDM 2017)Neural Text Embeddings for Information Retrieval (WSDM 2017)
Neural Text Embeddings for Information Retrieval (WSDM 2017)
 
Natural Language Processing in R (rNLP)
Natural Language Processing in R (rNLP)Natural Language Processing in R (rNLP)
Natural Language Processing in R (rNLP)
 
SSSW 2013 - Feeding Recommender Systems with Linked Open Data
SSSW 2013 - Feeding Recommender Systems with Linked Open DataSSSW 2013 - Feeding Recommender Systems with Linked Open Data
SSSW 2013 - Feeding Recommender Systems with Linked Open Data
 
Democratizing Big Semantic Data management
Democratizing Big Semantic Data managementDemocratizing Big Semantic Data management
Democratizing Big Semantic Data management
 
Stanford'12 Intro to Ontology Based Data Access for RDBMS through query rewri...
Stanford'12 Intro to Ontology Based Data Access for RDBMS through query rewri...Stanford'12 Intro to Ontology Based Data Access for RDBMS through query rewri...
Stanford'12 Intro to Ontology Based Data Access for RDBMS through query rewri...
 
DB and IR Integration
DB and IR IntegrationDB and IR Integration
DB and IR Integration
 
Open nlp presentationss
Open nlp presentationssOpen nlp presentationss
Open nlp presentationss
 
Some Information Retrieval Models and Our Experiments for TREC KBA
Some Information Retrieval Models and Our Experiments for TREC KBASome Information Retrieval Models and Our Experiments for TREC KBA
Some Information Retrieval Models and Our Experiments for TREC KBA
 

More from Jie Bao

python-graph-lovestory
python-graph-lovestorypython-graph-lovestory
python-graph-lovestoryJie Bao
 
unix toolbox 中文版
unix toolbox 中文版unix toolbox 中文版
unix toolbox 中文版Jie Bao
 
unixtoolbox.book
unixtoolbox.bookunixtoolbox.book
unixtoolbox.bookJie Bao
 
Lean startup 精益创业 新创企业的成长思维
Lean startup 精益创业 新创企业的成长思维Lean startup 精益创业 新创企业的成长思维
Lean startup 精益创业 新创企业的成长思维Jie Bao
 
Towards social webtops using semantic wiki
Towards social webtops using semantic wikiTowards social webtops using semantic wiki
Towards social webtops using semantic wikiJie Bao
 
Semantic information theory in 20 minutes
Semantic information theory in 20 minutesSemantic information theory in 20 minutes
Semantic information theory in 20 minutesJie Bao
 
Towards a theory of semantic communication
Towards a theory of semantic communicationTowards a theory of semantic communication
Towards a theory of semantic communicationJie Bao
 
Expressive Query Answering For Semantic Wikis (20min)
Expressive Query Answering For  Semantic Wikis (20min)Expressive Query Answering For  Semantic Wikis (20min)
Expressive Query Answering For Semantic Wikis (20min)Jie Bao
 
Startup best practices
Startup best practicesStartup best practices
Startup best practicesJie Bao
 
Owl 2 quick reference card a4 size
Owl 2 quick reference card a4 sizeOwl 2 quick reference card a4 size
Owl 2 quick reference card a4 sizeJie Bao
 
ISWC 2010 Metadata Work Summary
ISWC 2010 Metadata Work SummaryISWC 2010 Metadata Work Summary
ISWC 2010 Metadata Work SummaryJie Bao
 
Expressive Query Answering For Semantic Wikis
Expressive Query Answering For  Semantic WikisExpressive Query Answering For  Semantic Wikis
Expressive Query Answering For Semantic WikisJie Bao
 
24 Ways to Explore ISWC 2010 Data
24 Ways to Explore ISWC 2010 Data24 Ways to Explore ISWC 2010 Data
24 Ways to Explore ISWC 2010 DataJie Bao
 
Semantic Web: In Quest for the Next Generation Killer Apps
Semantic Web: In Quest for the Next Generation Killer AppsSemantic Web: In Quest for the Next Generation Killer Apps
Semantic Web: In Quest for the Next Generation Killer AppsJie Bao
 
Representing financial reports on the semantic web a faithful translation f...
Representing financial reports on the semantic web   a faithful translation f...Representing financial reports on the semantic web   a faithful translation f...
Representing financial reports on the semantic web a faithful translation f...Jie Bao
 
XACML 3.0 (Partial) Concept Map
XACML 3.0 (Partial) Concept MapXACML 3.0 (Partial) Concept Map
XACML 3.0 (Partial) Concept MapJie Bao
 
Development of a Controlled Natural Language Interface for Semantic MediaWiki
Development of a Controlled Natural Language Interface for Semantic MediaWikiDevelopment of a Controlled Natural Language Interface for Semantic MediaWiki
Development of a Controlled Natural Language Interface for Semantic MediaWikiJie Bao
 
Digital image self-adaptive acquisition in medical x-ray imaging
Digital image self-adaptive acquisition in medical x-ray imagingDigital image self-adaptive acquisition in medical x-ray imaging
Digital image self-adaptive acquisition in medical x-ray imagingJie Bao
 
Privacy-Preserving Reasoning on the Semantic Web (Poster)
Privacy-Preserving Reasoning on the Semantic Web (Poster)Privacy-Preserving Reasoning on the Semantic Web (Poster)
Privacy-Preserving Reasoning on the Semantic Web (Poster)Jie Bao
 

More from Jie Bao (20)

python-graph-lovestory
python-graph-lovestorypython-graph-lovestory
python-graph-lovestory
 
unix toolbox 中文版
unix toolbox 中文版unix toolbox 中文版
unix toolbox 中文版
 
unixtoolbox.book
unixtoolbox.bookunixtoolbox.book
unixtoolbox.book
 
Lean startup 精益创业 新创企业的成长思维
Lean startup 精益创业 新创企业的成长思维Lean startup 精益创业 新创企业的成长思维
Lean startup 精益创业 新创企业的成长思维
 
Towards social webtops using semantic wiki
Towards social webtops using semantic wikiTowards social webtops using semantic wiki
Towards social webtops using semantic wiki
 
Semantic information theory in 20 minutes
Semantic information theory in 20 minutesSemantic information theory in 20 minutes
Semantic information theory in 20 minutes
 
Towards a theory of semantic communication
Towards a theory of semantic communicationTowards a theory of semantic communication
Towards a theory of semantic communication
 
Expressive Query Answering For Semantic Wikis (20min)
Expressive Query Answering For  Semantic Wikis (20min)Expressive Query Answering For  Semantic Wikis (20min)
Expressive Query Answering For Semantic Wikis (20min)
 
Startup best practices
Startup best practicesStartup best practices
Startup best practices
 
Owl 2 quick reference card a4 size
Owl 2 quick reference card a4 sizeOwl 2 quick reference card a4 size
Owl 2 quick reference card a4 size
 
ISWC 2010 Metadata Work Summary
ISWC 2010 Metadata Work SummaryISWC 2010 Metadata Work Summary
ISWC 2010 Metadata Work Summary
 
Expressive Query Answering For Semantic Wikis
Expressive Query Answering For  Semantic WikisExpressive Query Answering For  Semantic Wikis
Expressive Query Answering For Semantic Wikis
 
CV
CVCV
CV
 
24 Ways to Explore ISWC 2010 Data
24 Ways to Explore ISWC 2010 Data24 Ways to Explore ISWC 2010 Data
24 Ways to Explore ISWC 2010 Data
 
Semantic Web: In Quest for the Next Generation Killer Apps
Semantic Web: In Quest for the Next Generation Killer AppsSemantic Web: In Quest for the Next Generation Killer Apps
Semantic Web: In Quest for the Next Generation Killer Apps
 
Representing financial reports on the semantic web a faithful translation f...
Representing financial reports on the semantic web   a faithful translation f...Representing financial reports on the semantic web   a faithful translation f...
Representing financial reports on the semantic web a faithful translation f...
 
XACML 3.0 (Partial) Concept Map
XACML 3.0 (Partial) Concept MapXACML 3.0 (Partial) Concept Map
XACML 3.0 (Partial) Concept Map
 
Development of a Controlled Natural Language Interface for Semantic MediaWiki
Development of a Controlled Natural Language Interface for Semantic MediaWikiDevelopment of a Controlled Natural Language Interface for Semantic MediaWiki
Development of a Controlled Natural Language Interface for Semantic MediaWiki
 
Digital image self-adaptive acquisition in medical x-ray imaging
Digital image self-adaptive acquisition in medical x-ray imagingDigital image self-adaptive acquisition in medical x-ray imaging
Digital image self-adaptive acquisition in medical x-ray imaging
 
Privacy-Preserving Reasoning on the Semantic Web (Poster)
Privacy-Preserving Reasoning on the Semantic Web (Poster)Privacy-Preserving Reasoning on the Semantic Web (Poster)
Privacy-Preserving Reasoning on the Semantic Web (Poster)
 

Recently uploaded

Procuring digital preservation CAN be quick and painless with our new dynamic...
Procuring digital preservation CAN be quick and painless with our new dynamic...Procuring digital preservation CAN be quick and painless with our new dynamic...
Procuring digital preservation CAN be quick and painless with our new dynamic...Jisc
 
Difference Between Search & Browse Methods in Odoo 17
Difference Between Search & Browse Methods in Odoo 17Difference Between Search & Browse Methods in Odoo 17
Difference Between Search & Browse Methods in Odoo 17Celine George
 
Choosing the Right CBSE School A Comprehensive Guide for Parents
Choosing the Right CBSE School A Comprehensive Guide for ParentsChoosing the Right CBSE School A Comprehensive Guide for Parents
Choosing the Right CBSE School A Comprehensive Guide for Parentsnavabharathschool99
 
Student Profile Sample - We help schools to connect the data they have, with ...
Student Profile Sample - We help schools to connect the data they have, with ...Student Profile Sample - We help schools to connect the data they have, with ...
Student Profile Sample - We help schools to connect the data they have, with ...Seán Kennedy
 
call girls in Kamla Market (DELHI) 🔝 >༒9953330565🔝 genuine Escort Service 🔝✔️✔️
call girls in Kamla Market (DELHI) 🔝 >༒9953330565🔝 genuine Escort Service 🔝✔️✔️call girls in Kamla Market (DELHI) 🔝 >༒9953330565🔝 genuine Escort Service 🔝✔️✔️
call girls in Kamla Market (DELHI) 🔝 >༒9953330565🔝 genuine Escort Service 🔝✔️✔️9953056974 Low Rate Call Girls In Saket, Delhi NCR
 
Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17
Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17
Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17Celine George
 
HỌC TỐT TIẾNG ANH 11 THEO CHƯƠNG TRÌNH GLOBAL SUCCESS ĐÁP ÁN CHI TIẾT - CẢ NĂ...
HỌC TỐT TIẾNG ANH 11 THEO CHƯƠNG TRÌNH GLOBAL SUCCESS ĐÁP ÁN CHI TIẾT - CẢ NĂ...HỌC TỐT TIẾNG ANH 11 THEO CHƯƠNG TRÌNH GLOBAL SUCCESS ĐÁP ÁN CHI TIẾT - CẢ NĂ...
HỌC TỐT TIẾNG ANH 11 THEO CHƯƠNG TRÌNH GLOBAL SUCCESS ĐÁP ÁN CHI TIẾT - CẢ NĂ...Nguyen Thanh Tu Collection
 
Concurrency Control in Database Management system
Concurrency Control in Database Management systemConcurrency Control in Database Management system
Concurrency Control in Database Management systemChristalin Nelson
 
FILIPINO PSYCHology sikolohiyang pilipino
FILIPINO PSYCHology sikolohiyang pilipinoFILIPINO PSYCHology sikolohiyang pilipino
FILIPINO PSYCHology sikolohiyang pilipinojohnmickonozaleda
 
What is Model Inheritance in Odoo 17 ERP
What is Model Inheritance in Odoo 17 ERPWhat is Model Inheritance in Odoo 17 ERP
What is Model Inheritance in Odoo 17 ERPCeline George
 
Virtual-Orientation-on-the-Administration-of-NATG12-NATG6-and-ELLNA.pdf
Virtual-Orientation-on-the-Administration-of-NATG12-NATG6-and-ELLNA.pdfVirtual-Orientation-on-the-Administration-of-NATG12-NATG6-and-ELLNA.pdf
Virtual-Orientation-on-the-Administration-of-NATG12-NATG6-and-ELLNA.pdfErwinPantujan2
 
How to Add Barcode on PDF Report in Odoo 17
How to Add Barcode on PDF Report in Odoo 17How to Add Barcode on PDF Report in Odoo 17
How to Add Barcode on PDF Report in Odoo 17Celine George
 
Influencing policy (training slides from Fast Track Impact)
Influencing policy (training slides from Fast Track Impact)Influencing policy (training slides from Fast Track Impact)
Influencing policy (training slides from Fast Track Impact)Mark Reed
 
Visit to a blind student's school🧑‍🦯🧑‍🦯(community medicine)
Visit to a blind student's school🧑‍🦯🧑‍🦯(community medicine)Visit to a blind student's school🧑‍🦯🧑‍🦯(community medicine)
Visit to a blind student's school🧑‍🦯🧑‍🦯(community medicine)lakshayb543
 
AMERICAN LANGUAGE HUB_Level2_Student'sBook_Answerkey.pdf
AMERICAN LANGUAGE HUB_Level2_Student'sBook_Answerkey.pdfAMERICAN LANGUAGE HUB_Level2_Student'sBook_Answerkey.pdf
AMERICAN LANGUAGE HUB_Level2_Student'sBook_Answerkey.pdfphamnguyenenglishnb
 

Recently uploaded (20)

Procuring digital preservation CAN be quick and painless with our new dynamic...
Procuring digital preservation CAN be quick and painless with our new dynamic...Procuring digital preservation CAN be quick and painless with our new dynamic...
Procuring digital preservation CAN be quick and painless with our new dynamic...
 
Difference Between Search & Browse Methods in Odoo 17
Difference Between Search & Browse Methods in Odoo 17Difference Between Search & Browse Methods in Odoo 17
Difference Between Search & Browse Methods in Odoo 17
 
Choosing the Right CBSE School A Comprehensive Guide for Parents
Choosing the Right CBSE School A Comprehensive Guide for ParentsChoosing the Right CBSE School A Comprehensive Guide for Parents
Choosing the Right CBSE School A Comprehensive Guide for Parents
 
Student Profile Sample - We help schools to connect the data they have, with ...
Student Profile Sample - We help schools to connect the data they have, with ...Student Profile Sample - We help schools to connect the data they have, with ...
Student Profile Sample - We help schools to connect the data they have, with ...
 
call girls in Kamla Market (DELHI) 🔝 >༒9953330565🔝 genuine Escort Service 🔝✔️✔️
call girls in Kamla Market (DELHI) 🔝 >༒9953330565🔝 genuine Escort Service 🔝✔️✔️call girls in Kamla Market (DELHI) 🔝 >༒9953330565🔝 genuine Escort Service 🔝✔️✔️
call girls in Kamla Market (DELHI) 🔝 >༒9953330565🔝 genuine Escort Service 🔝✔️✔️
 
Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17
Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17
Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17
 
HỌC TỐT TIẾNG ANH 11 THEO CHƯƠNG TRÌNH GLOBAL SUCCESS ĐÁP ÁN CHI TIẾT - CẢ NĂ...
HỌC TỐT TIẾNG ANH 11 THEO CHƯƠNG TRÌNH GLOBAL SUCCESS ĐÁP ÁN CHI TIẾT - CẢ NĂ...HỌC TỐT TIẾNG ANH 11 THEO CHƯƠNG TRÌNH GLOBAL SUCCESS ĐÁP ÁN CHI TIẾT - CẢ NĂ...
HỌC TỐT TIẾNG ANH 11 THEO CHƯƠNG TRÌNH GLOBAL SUCCESS ĐÁP ÁN CHI TIẾT - CẢ NĂ...
 
Concurrency Control in Database Management system
Concurrency Control in Database Management systemConcurrency Control in Database Management system
Concurrency Control in Database Management system
 
Raw materials used in Herbal Cosmetics.pptx
Raw materials used in Herbal Cosmetics.pptxRaw materials used in Herbal Cosmetics.pptx
Raw materials used in Herbal Cosmetics.pptx
 
FINALS_OF_LEFT_ON_C'N_EL_DORADO_2024.pptx
FINALS_OF_LEFT_ON_C'N_EL_DORADO_2024.pptxFINALS_OF_LEFT_ON_C'N_EL_DORADO_2024.pptx
FINALS_OF_LEFT_ON_C'N_EL_DORADO_2024.pptx
 
FILIPINO PSYCHology sikolohiyang pilipino
FILIPINO PSYCHology sikolohiyang pilipinoFILIPINO PSYCHology sikolohiyang pilipino
FILIPINO PSYCHology sikolohiyang pilipino
 
What is Model Inheritance in Odoo 17 ERP
What is Model Inheritance in Odoo 17 ERPWhat is Model Inheritance in Odoo 17 ERP
What is Model Inheritance in Odoo 17 ERP
 
Virtual-Orientation-on-the-Administration-of-NATG12-NATG6-and-ELLNA.pdf
Virtual-Orientation-on-the-Administration-of-NATG12-NATG6-and-ELLNA.pdfVirtual-Orientation-on-the-Administration-of-NATG12-NATG6-and-ELLNA.pdf
Virtual-Orientation-on-the-Administration-of-NATG12-NATG6-and-ELLNA.pdf
 
How to Add Barcode on PDF Report in Odoo 17
How to Add Barcode on PDF Report in Odoo 17How to Add Barcode on PDF Report in Odoo 17
How to Add Barcode on PDF Report in Odoo 17
 
Model Call Girl in Tilak Nagar Delhi reach out to us at 🔝9953056974🔝
Model Call Girl in Tilak Nagar Delhi reach out to us at 🔝9953056974🔝Model Call Girl in Tilak Nagar Delhi reach out to us at 🔝9953056974🔝
Model Call Girl in Tilak Nagar Delhi reach out to us at 🔝9953056974🔝
 
LEFT_ON_C'N_ PRELIMS_EL_DORADO_2024.pptx
LEFT_ON_C'N_ PRELIMS_EL_DORADO_2024.pptxLEFT_ON_C'N_ PRELIMS_EL_DORADO_2024.pptx
LEFT_ON_C'N_ PRELIMS_EL_DORADO_2024.pptx
 
YOUVE_GOT_EMAIL_PRELIMS_EL_DORADO_2024.pptx
YOUVE_GOT_EMAIL_PRELIMS_EL_DORADO_2024.pptxYOUVE_GOT_EMAIL_PRELIMS_EL_DORADO_2024.pptx
YOUVE_GOT_EMAIL_PRELIMS_EL_DORADO_2024.pptx
 
Influencing policy (training slides from Fast Track Impact)
Influencing policy (training slides from Fast Track Impact)Influencing policy (training slides from Fast Track Impact)
Influencing policy (training slides from Fast Track Impact)
 
Visit to a blind student's school🧑‍🦯🧑‍🦯(community medicine)
Visit to a blind student's school🧑‍🦯🧑‍🦯(community medicine)Visit to a blind student's school🧑‍🦯🧑‍🦯(community medicine)
Visit to a blind student's school🧑‍🦯🧑‍🦯(community medicine)
 
AMERICAN LANGUAGE HUB_Level2_Student'sBook_Answerkey.pdf
AMERICAN LANGUAGE HUB_Level2_Student'sBook_Answerkey.pdfAMERICAN LANGUAGE HUB_Level2_Student'sBook_Answerkey.pdf
AMERICAN LANGUAGE HUB_Level2_Student'sBook_Answerkey.pdf
 

Query Translation for Ontology-extended Data Sources

  • 1. Query Translation for Ontology-extended Data Sources Jie Bao 1 , Doina Caragea 2 , Vasant Honavar 1 1 Artificial Intelligence Research Laboratory, Department of Computer Science, Iowa State University, Ames, IA 50011-1040, USA {baojie, honavar}@cs.iastate.edu 2 Department of Computing and Information Sciences Kansas State University, Manhattan, KS 66506, USA {dcaragea}@ksu.edu
  • 2. INDUS Group Vasant Honavar Jie Bao Doina Caragea Jyotishman Pathak Neeraj Koul
  • 3.
  • 4.
  • 5. Solution: INDUS for Learning from Semantically Heterogeneous Distributed Autonomous Data Sources
  • 6. (Relational) Data Source D Data Set Extensional Definition (Facts) MSc Bob First-year Alice status name Student algorithm CS511 data structure CS103 name code Classes CS511 Bob CS103 Alice class instructor Registers S Schema Intensional Definition Classes Faculty Teaches name:String code:String rank:String name:String Student Registers name:String status:String
  • 7. Semantic Extensions of Data Sources Return classes that graduate students are registered in Return all people in the database ? ? D S MSc Bob First-year Alice status name Student algorithm CS511 data structure CS103 name code Classes CS511 Bob CS103 Alice class instructor Registers
  • 8. Ontology-Extended Data Source Classes Instructor Teaches name:String code:String rank:String name:String Student registers name:String status:String People Student Instructor MSc Bob First-year Alice status name Student student Undergrad Graduate First- year MSc Fourth- year … PhD MA
  • 9. Ontology-Extended Data Source D Data Set S Schema O S Schema Ontology O D Data Content Ontology O’ S O’ D
  • 10.
  • 11. OEDS: Example S: Instructor(x,y); Classes(x,y), Student(x,y)… see survey [Shvaiko & Euzenat 2005] MSc Bob First-year Alice status name Student D Classes Instructor Teaches name:String code:String rank:String name:String Student registers name:String status:String L OS  x,y, Student(x,y)  Instructor(x,y)  People(x) isa(x,y)  isa(y,z)  isa(x,z) L OD D OD isa(First-year,Undergraduate) isa(Undergraduate,Student) isa(MSc,Graduate) … O D
  • 12.
  • 13.
  • 14. Query Translation D S O 2 q’ D S q O 1 User Ontology Data Source Ontology M Ontology Mapping
  • 15.
  • 16. Query Translation Student(t) ^ isa 1 (t:status,Master) Student(t) ^ isa 2 (t.status, Graduate) Student(t) ^ isa 2 (t.status, MSc) D S O 2 q’ q D S O 1 M
  • 17. Soundness, Completeness and Exactness {q} {q’} {q’} {q} {q}={q’} Sound Translation Complete Translation Exact Translation q := Student(t) ^ isa 1 (t:status,Master) q’ := Student(t) ^ isa 2 (t.status, MSc) q’ := Student(t) ^ isa 2 (t.status, Graduate) Non-existent
  • 18. Most Informative Translation c 1 d 1 d 2 O 1 O 2 q := isa 1 (x,c 1 ) isa 2 (x,d 1 )  isa 2 (x,d 2 ) Most informative sound translation! onto onto LUB (least upper bound) isa 2 (x,d 1 ) isa 2 (x,d 2 ) find its sound translation(s)
  • 19.
  • 20.
  • 21.
  • 22. INDUS – Mapping Editor http://sourceforge.net/projects/indus-project/
  • 23. INDUS – Data Editor http://sourceforge.net/projects/indus-project/
  • 24. INDUS – Query Editor http://sourceforge.net/projects/indus-project/
  • 25.
  • 26.
  • 28.
  • 29.
  • 30.
  • 31.