SlideShare une entreprise Scribd logo
1  sur  9
Télécharger pour lire hors ligne
OAK Code Days: RDF query reformulation algorithm 
OAK Code Days: RDF query reformulation 
algorithm 
owner: Damian Bursztyn 
presenter: Raphaël Bonaque 
September 16th 2014 
1 / 9
OAK Code Days: RDF query reformulation algorithm 
What does it do ? 
What 
Rewrite a certain class of queries into a list of queries equivalent in 
different settings: 
Input: 
a conjunctive RDF query 
an RDF Schema 
Output: a union of conjunctive queries such that 
Q0(D) = Q(Dinf ) i.e. it is equivalent, on a set of data triples, 
with the original query on the saturation of this triples with 
the RDF Schema 
2 / 9
OAK Code Days: RDF query reformulation algorithm 
What does it do ? 
The code 
All in JAVA 
Code history: 
First monolithic version by Konstantinos and Julian 
Modular rewriting by Damian 
Performance improvements by Damian 
Location: on GForge at https://scm.gforge.inria.fr/ 
svn/distriples/trunk/Reformulator/ 
source code unitary tests total 
number of lines 2722 1272 3994 
number of classes 30 15 45 
3 / 9
OAK Code Days: RDF query reformulation algorithm 
Who is dealing with the code ? 
Who 
Wrote it: 
Damian 
Julian 
Konstantinos 
Is using it: 
Damian 
(Alexandra, with an old version, in Sat. versus Ref.) 
4 / 9
OAK Code Days: RDF query reformulation algorithm 
How does it work ? 
General overview 
How: overview 
6 packages: 
reasoning: the general entry point of the code, contains: 
schema: models RDF Schemas 
a reasoner 
a much faster concurrent version of reasoner (up to 12.5 times 
faster) 
rules: contains the rewriting rules 
rulesets: basically a collection of rules (maintained for 
compatibility) 
conjunctive query signature: some queries are equivalent we 
want to avoid duplicates 
Equivalence hard to compute (NP), signature easier, less 
powerfull (all queries with same signature are equivalent, some 
equivalent queries have different signatures) 
exceptions 
utilities: some minor utilities 
5 / 9
OAK Code Days: RDF query reformulation algorithm 
How does it work ? 
Libraries 
How: libraries 
Jena: used for instance for constants like 
http://www.w3.org/1999/02/22-rdf-syntax-ns#type and to 
model the schema 
Xerces: for parsing RDF schema (Requieres icu and iri) 
2 logging libraries: 
log4j the classical logger used by the team 
slf4j to log Xerces 
6 / 9
OAK Code Days: RDF query reformulation algorithm 
Litterature 
Which papers 
View Selection in Semantic Web Databases by François 
Goasdoué, Konstantinos Karanasos, Julien Leblay and Ioana 
Manolescu 
Efficient Query Answering against Dynamic RDF Databases 
by François Goasdoué, Ioana Manolescu, Alexandra Roatis 
7 / 9
OAK Code Days: RDF query reformulation algorithm 
Next things to do 
TODOs & bugs 
No known bug 
Create a test that checks the content of a reformulation, not 
only its size 
8 / 9
OAK Code Days: RDF query reformulation algorithm 
Conclusion 
Questions ? 
Thank you for listening ! 
9 / 9

Contenu connexe

Tendances

STAT Requirement Analysis
STAT Requirement AnalysisSTAT Requirement Analysis
STAT Requirement Analysisstat
 
Batch import of large RDF datasets into Semantic MediaWiki
Batch import of large RDF datasets into Semantic MediaWikiBatch import of large RDF datasets into Semantic MediaWiki
Batch import of large RDF datasets into Semantic MediaWikiSamuel Lampa
 
DLF 2015 Presentation, "RDF in the Real World."
DLF 2015 Presentation, "RDF in the Real World." DLF 2015 Presentation, "RDF in the Real World."
DLF 2015 Presentation, "RDF in the Real World." Avalon Media System
 
Hooking up Semantic MediaWiki with external tools via SPARQL
Hooking up Semantic MediaWiki with external tools via SPARQLHooking up Semantic MediaWiki with external tools via SPARQL
Hooking up Semantic MediaWiki with external tools via SPARQLSamuel Lampa
 
Creating R Packages
Creating R PackagesCreating R Packages
Creating R Packagesjalle6
 
WISE2017 - Factorization Machines Leveraging Lightweight Linked Open Data-ena...
WISE2017 - Factorization Machines Leveraging Lightweight Linked Open Data-ena...WISE2017 - Factorization Machines Leveraging Lightweight Linked Open Data-ena...
WISE2017 - Factorization Machines Leveraging Lightweight Linked Open Data-ena...GUANGYUAN PIAO
 
Code4 lib 20141129 python
Code4 lib 20141129 pythonCode4 lib 20141129 python
Code4 lib 20141129 pythontdsmithCapU
 
Integration of collection data - A case study from the Oxford Museums and Lib...
Integration of collection data - A case study from the Oxford Museums and Lib...Integration of collection data - A case study from the Oxford Museums and Lib...
Integration of collection data - A case study from the Oxford Museums and Lib...Athanasios Velios
 
2008 11 13 Hcls Call
2008 11 13 Hcls Call2008 11 13 Hcls Call
2008 11 13 Hcls CallJun Zhao
 
Poster Declaratively Describing Responses of Hypermedia-Driven Web APIs
Poster Declaratively Describing Responses of Hypermedia-Driven Web APIsPoster Declaratively Describing Responses of Hypermedia-Driven Web APIs
Poster Declaratively Describing Responses of Hypermedia-Driven Web APIsRuben Taelman
 
FedX - Optimization Techniques for Federated Query Processing on Linked Data
FedX - Optimization Techniques for Federated Query Processing on Linked DataFedX - Optimization Techniques for Federated Query Processing on Linked Data
FedX - Optimization Techniques for Federated Query Processing on Linked Dataaschwarte
 
Comparing open source search engines
Comparing open source search enginesComparing open source search engines
Comparing open source search enginesRichard Boulton
 
Federated SPARQL Query Processing ISWC2015 Tutorial
Federated SPARQL Query Processing ISWC2015 TutorialFederated SPARQL Query Processing ISWC2015 Tutorial
Federated SPARQL Query Processing ISWC2015 TutorialMuhammad Saleem
 
Federated SPARQL query processing over the Web of Data
Federated SPARQL query processing over the Web of DataFederated SPARQL query processing over the Web of Data
Federated SPARQL query processing over the Web of DataMuhammad Saleem
 

Tendances (20)

STAT Requirement Analysis
STAT Requirement AnalysisSTAT Requirement Analysis
STAT Requirement Analysis
 
Fedora Migration Considerations
Fedora Migration ConsiderationsFedora Migration Considerations
Fedora Migration Considerations
 
Batch import of large RDF datasets into Semantic MediaWiki
Batch import of large RDF datasets into Semantic MediaWikiBatch import of large RDF datasets into Semantic MediaWiki
Batch import of large RDF datasets into Semantic MediaWiki
 
DLF 2015 Presentation, "RDF in the Real World."
DLF 2015 Presentation, "RDF in the Real World." DLF 2015 Presentation, "RDF in the Real World."
DLF 2015 Presentation, "RDF in the Real World."
 
Hooking up Semantic MediaWiki with external tools via SPARQL
Hooking up Semantic MediaWiki with external tools via SPARQLHooking up Semantic MediaWiki with external tools via SPARQL
Hooking up Semantic MediaWiki with external tools via SPARQL
 
FRP vs CSP
FRP vs CSPFRP vs CSP
FRP vs CSP
 
Creating R Packages
Creating R PackagesCreating R Packages
Creating R Packages
 
Files c3
Files c3Files c3
Files c3
 
Guide to ROS tools
Guide to ROS tools Guide to ROS tools
Guide to ROS tools
 
WISE2017 - Factorization Machines Leveraging Lightweight Linked Open Data-ena...
WISE2017 - Factorization Machines Leveraging Lightweight Linked Open Data-ena...WISE2017 - Factorization Machines Leveraging Lightweight Linked Open Data-ena...
WISE2017 - Factorization Machines Leveraging Lightweight Linked Open Data-ena...
 
Code4 lib 20141129 python
Code4 lib 20141129 pythonCode4 lib 20141129 python
Code4 lib 20141129 python
 
Lzw algorithm
Lzw algorithmLzw algorithm
Lzw algorithm
 
Integration of collection data - A case study from the Oxford Museums and Lib...
Integration of collection data - A case study from the Oxford Museums and Lib...Integration of collection data - A case study from the Oxford Museums and Lib...
Integration of collection data - A case study from the Oxford Museums and Lib...
 
2008 11 13 Hcls Call
2008 11 13 Hcls Call2008 11 13 Hcls Call
2008 11 13 Hcls Call
 
Poster Declaratively Describing Responses of Hypermedia-Driven Web APIs
Poster Declaratively Describing Responses of Hypermedia-Driven Web APIsPoster Declaratively Describing Responses of Hypermedia-Driven Web APIs
Poster Declaratively Describing Responses of Hypermedia-Driven Web APIs
 
FedX - Optimization Techniques for Federated Query Processing on Linked Data
FedX - Optimization Techniques for Federated Query Processing on Linked DataFedX - Optimization Techniques for Federated Query Processing on Linked Data
FedX - Optimization Techniques for Federated Query Processing on Linked Data
 
Comparing open source search engines
Comparing open source search enginesComparing open source search engines
Comparing open source search engines
 
Federated SPARQL Query Processing ISWC2015 Tutorial
Federated SPARQL Query Processing ISWC2015 TutorialFederated SPARQL Query Processing ISWC2015 Tutorial
Federated SPARQL Query Processing ISWC2015 Tutorial
 
GooglePropsal
GooglePropsalGooglePropsal
GooglePropsal
 
Federated SPARQL query processing over the Web of Data
Federated SPARQL query processing over the Web of DataFederated SPARQL query processing over the Web of Data
Federated SPARQL query processing over the Web of Data
 

En vedette

Ipaw14 presentation Quan, Tanu, Ian
Ipaw14 presentation Quan, Tanu, IanIpaw14 presentation Quan, Tanu, Ian
Ipaw14 presentation Quan, Tanu, IanBoris Glavic
 
[ABDO] Data Integration
[ABDO] Data Integration[ABDO] Data Integration
[ABDO] Data IntegrationCarles Farré
 
TaPP 2015 - Towards Constraint-based Explanations for Answers and Non-Answers
TaPP 2015 - Towards Constraint-based Explanations for Answers and Non-AnswersTaPP 2015 - Towards Constraint-based Explanations for Answers and Non-Answers
TaPP 2015 - Towards Constraint-based Explanations for Answers and Non-AnswersBoris Glavic
 
EDBT 2009 - Provenance for Nested Subqueries
EDBT 2009 - Provenance for Nested SubqueriesEDBT 2009 - Provenance for Nested Subqueries
EDBT 2009 - Provenance for Nested SubqueriesBoris Glavic
 
RDF Analytics: Lenses over Semantic Graphs
RDF Analytics: Lenses over Semantic GraphsRDF Analytics: Lenses over Semantic Graphs
RDF Analytics: Lenses over Semantic GraphsAlexandra Roatiș
 
security analysis and investment management
security analysis and investment managementsecurity analysis and investment management
security analysis and investment managementkalathilvipin30
 

En vedette (6)

Ipaw14 presentation Quan, Tanu, Ian
Ipaw14 presentation Quan, Tanu, IanIpaw14 presentation Quan, Tanu, Ian
Ipaw14 presentation Quan, Tanu, Ian
 
[ABDO] Data Integration
[ABDO] Data Integration[ABDO] Data Integration
[ABDO] Data Integration
 
TaPP 2015 - Towards Constraint-based Explanations for Answers and Non-Answers
TaPP 2015 - Towards Constraint-based Explanations for Answers and Non-AnswersTaPP 2015 - Towards Constraint-based Explanations for Answers and Non-Answers
TaPP 2015 - Towards Constraint-based Explanations for Answers and Non-Answers
 
EDBT 2009 - Provenance for Nested Subqueries
EDBT 2009 - Provenance for Nested SubqueriesEDBT 2009 - Provenance for Nested Subqueries
EDBT 2009 - Provenance for Nested Subqueries
 
RDF Analytics: Lenses over Semantic Graphs
RDF Analytics: Lenses over Semantic GraphsRDF Analytics: Lenses over Semantic Graphs
RDF Analytics: Lenses over Semantic Graphs
 
security analysis and investment management
security analysis and investment managementsecurity analysis and investment management
security analysis and investment management
 

Similaire à rdf query reformulation

A Hands On Overview Of The Semantic Web
A Hands On Overview Of The Semantic WebA Hands On Overview Of The Semantic Web
A Hands On Overview Of The Semantic WebShamod Lacoul
 
A year on the Semantic Web @ W3C
A year on the Semantic Web @ W3CA year on the Semantic Web @ W3C
A year on the Semantic Web @ W3CIvan Herman
 
Rdf Processing Tools In Java
Rdf Processing Tools In JavaRdf Processing Tools In Java
Rdf Processing Tools In JavaDicusarCorneliu
 
Comparative Study That Aims Rdf Processing For The Java Platform
Comparative Study That Aims Rdf Processing For The Java PlatformComparative Study That Aims Rdf Processing For The Java Platform
Comparative Study That Aims Rdf Processing For The Java PlatformComputer Science
 
Comparative study on the processing of RDF in PHP
Comparative study on the processing of RDF in PHPComparative study on the processing of RDF in PHP
Comparative study on the processing of RDF in PHPMSGUNC
 
Modern PHP RDF toolkits: a comparative study
Modern PHP RDF toolkits: a comparative studyModern PHP RDF toolkits: a comparative study
Modern PHP RDF toolkits: a comparative studyMarius Butuc
 
2009 0807 Lod Gmod
2009 0807 Lod Gmod2009 0807 Lod Gmod
2009 0807 Lod GmodJun Zhao
 
Publishing RDF SKOS with microservices
Publishing RDF SKOS with microservicesPublishing RDF SKOS with microservices
Publishing RDF SKOS with microservicesBart Hanssens
 
RDF APIs for .NET Framework
RDF APIs for .NET FrameworkRDF APIs for .NET Framework
RDF APIs for .NET FrameworkAdriana Ivanciu
 
Supporting developers with natural language queries
Supporting developers with natural language queriesSupporting developers with natural language queries
Supporting developers with natural language queriesMasud Rahman
 
Publishing Linked Data 3/5 Semtech2011
Publishing Linked Data 3/5 Semtech2011Publishing Linked Data 3/5 Semtech2011
Publishing Linked Data 3/5 Semtech2011Juan Sequeda
 
Practical Cross-Dataset Queries with SPARQL (Introduction)
Practical Cross-Dataset Queries with SPARQL (Introduction)Practical Cross-Dataset Queries with SPARQL (Introduction)
Practical Cross-Dataset Queries with SPARQL (Introduction)Richard Cyganiak
 
RDA and the semantic Web
RDA and the semantic WebRDA and the semantic Web
RDA and the semantic WebGordon Dunsire
 
A Comparison Between Python APIs For RDF Processing
A Comparison Between Python APIs For RDF ProcessingA Comparison Between Python APIs For RDF Processing
A Comparison Between Python APIs For RDF Processinglucianb
 
MPTStore: A Fast, Scalable, and Stable Resource Index
MPTStore: A Fast, Scalable, and Stable Resource IndexMPTStore: A Fast, Scalable, and Stable Resource Index
MPTStore: A Fast, Scalable, and Stable Resource IndexChris Wilper
 

Similaire à rdf query reformulation (20)

A Hands On Overview Of The Semantic Web
A Hands On Overview Of The Semantic WebA Hands On Overview Of The Semantic Web
A Hands On Overview Of The Semantic Web
 
A year on the Semantic Web @ W3C
A year on the Semantic Web @ W3CA year on the Semantic Web @ W3C
A year on the Semantic Web @ W3C
 
Rdf Processing Tools In Java
Rdf Processing Tools In JavaRdf Processing Tools In Java
Rdf Processing Tools In Java
 
Comparative Study That Aims Rdf Processing For The Java Platform
Comparative Study That Aims Rdf Processing For The Java PlatformComparative Study That Aims Rdf Processing For The Java Platform
Comparative Study That Aims Rdf Processing For The Java Platform
 
.Net and Rdf APIs
.Net and Rdf APIs.Net and Rdf APIs
.Net and Rdf APIs
 
Comparative study on the processing of RDF in PHP
Comparative study on the processing of RDF in PHPComparative study on the processing of RDF in PHP
Comparative study on the processing of RDF in PHP
 
Modern PHP RDF toolkits: a comparative study
Modern PHP RDF toolkits: a comparative studyModern PHP RDF toolkits: a comparative study
Modern PHP RDF toolkits: a comparative study
 
Web Spa
Web SpaWeb Spa
Web Spa
 
2009 0807 Lod Gmod
2009 0807 Lod Gmod2009 0807 Lod Gmod
2009 0807 Lod Gmod
 
Publishing RDF SKOS with microservices
Publishing RDF SKOS with microservicesPublishing RDF SKOS with microservices
Publishing RDF SKOS with microservices
 
Introduction to dotNetRDF
Introduction to dotNetRDFIntroduction to dotNetRDF
Introduction to dotNetRDF
 
RDF APIs for .NET Framework
RDF APIs for .NET FrameworkRDF APIs for .NET Framework
RDF APIs for .NET Framework
 
Supporting developers with natural language queries
Supporting developers with natural language queriesSupporting developers with natural language queries
Supporting developers with natural language queries
 
Publishing Linked Data 3/5 Semtech2011
Publishing Linked Data 3/5 Semtech2011Publishing Linked Data 3/5 Semtech2011
Publishing Linked Data 3/5 Semtech2011
 
Practical Cross-Dataset Queries with SPARQL (Introduction)
Practical Cross-Dataset Queries with SPARQL (Introduction)Practical Cross-Dataset Queries with SPARQL (Introduction)
Practical Cross-Dataset Queries with SPARQL (Introduction)
 
Linked data and voyager
Linked data and voyagerLinked data and voyager
Linked data and voyager
 
RDA and the semantic Web
RDA and the semantic WebRDA and the semantic Web
RDA and the semantic Web
 
RDF and Java
RDF and JavaRDF and Java
RDF and Java
 
A Comparison Between Python APIs For RDF Processing
A Comparison Between Python APIs For RDF ProcessingA Comparison Between Python APIs For RDF Processing
A Comparison Between Python APIs For RDF Processing
 
MPTStore: A Fast, Scalable, and Stable Resource Index
MPTStore: A Fast, Scalable, and Stable Resource IndexMPTStore: A Fast, Scalable, and Stable Resource Index
MPTStore: A Fast, Scalable, and Stable Resource Index
 

Plus de INRIA-OAK

Change Management in the Traditional and Semantic Web
Change Management in the Traditional and Semantic WebChange Management in the Traditional and Semantic Web
Change Management in the Traditional and Semantic WebINRIA-OAK
 
A Network-Aware Approach for Searching As-You-Type in Social Media
A Network-Aware Approach for Searching As-You-Type in Social MediaA Network-Aware Approach for Searching As-You-Type in Social Media
A Network-Aware Approach for Searching As-You-Type in Social MediaINRIA-OAK
 
Speeding up information extraction programs: a holistic optimizer and a learn...
Speeding up information extraction programs: a holistic optimizer and a learn...Speeding up information extraction programs: a holistic optimizer and a learn...
Speeding up information extraction programs: a holistic optimizer and a learn...INRIA-OAK
 
Querying incomplete data
Querying incomplete dataQuerying incomplete data
Querying incomplete dataINRIA-OAK
 
ANGIE in wonderland
ANGIE in wonderlandANGIE in wonderland
ANGIE in wonderlandINRIA-OAK
 
On building more human query answering systems
On building more human query answering systemsOn building more human query answering systems
On building more human query answering systemsINRIA-OAK
 
Dynamically Optimizing Queries over Large Scale Data Platforms
Dynamically Optimizing Queries over Large Scale Data PlatformsDynamically Optimizing Queries over Large Scale Data Platforms
Dynamically Optimizing Queries over Large Scale Data PlatformsINRIA-OAK
 
Web Data Management in RDF Age
Web Data Management in RDF AgeWeb Data Management in RDF Age
Web Data Management in RDF AgeINRIA-OAK
 
Oak meeting 18/09/2014
Oak meeting 18/09/2014Oak meeting 18/09/2014
Oak meeting 18/09/2014INRIA-OAK
 
Rdf saturator
Rdf saturatorRdf saturator
Rdf saturatorINRIA-OAK
 
Rdf generator
Rdf generatorRdf generator
Rdf generatorINRIA-OAK
 
Rdf conjunctive query selectivity estimation
Rdf conjunctive query selectivity estimationRdf conjunctive query selectivity estimation
Rdf conjunctive query selectivity estimationINRIA-OAK
 
Conjunctive queries
Conjunctive queriesConjunctive queries
Conjunctive queriesINRIA-OAK
 
CliqueSquare processing
CliqueSquare processingCliqueSquare processing
CliqueSquare processingINRIA-OAK
 

Plus de INRIA-OAK (20)

Change Management in the Traditional and Semantic Web
Change Management in the Traditional and Semantic WebChange Management in the Traditional and Semantic Web
Change Management in the Traditional and Semantic Web
 
A Network-Aware Approach for Searching As-You-Type in Social Media
A Network-Aware Approach for Searching As-You-Type in Social MediaA Network-Aware Approach for Searching As-You-Type in Social Media
A Network-Aware Approach for Searching As-You-Type in Social Media
 
Speeding up information extraction programs: a holistic optimizer and a learn...
Speeding up information extraction programs: a holistic optimizer and a learn...Speeding up information extraction programs: a holistic optimizer and a learn...
Speeding up information extraction programs: a holistic optimizer and a learn...
 
Querying incomplete data
Querying incomplete dataQuerying incomplete data
Querying incomplete data
 
ANGIE in wonderland
ANGIE in wonderlandANGIE in wonderland
ANGIE in wonderland
 
On building more human query answering systems
On building more human query answering systemsOn building more human query answering systems
On building more human query answering systems
 
Dynamically Optimizing Queries over Large Scale Data Platforms
Dynamically Optimizing Queries over Large Scale Data PlatformsDynamically Optimizing Queries over Large Scale Data Platforms
Dynamically Optimizing Queries over Large Scale Data Platforms
 
Web Data Management in RDF Age
Web Data Management in RDF AgeWeb Data Management in RDF Age
Web Data Management in RDF Age
 
Oak meeting 18/09/2014
Oak meeting 18/09/2014Oak meeting 18/09/2014
Oak meeting 18/09/2014
 
Nautilus
NautilusNautilus
Nautilus
 
Warg
WargWarg
Warg
 
Vip2p
Vip2pVip2p
Vip2p
 
S4
S4S4
S4
 
Rdf saturator
Rdf saturatorRdf saturator
Rdf saturator
 
Rdf generator
Rdf generatorRdf generator
Rdf generator
 
Rdf conjunctive query selectivity estimation
Rdf conjunctive query selectivity estimationRdf conjunctive query selectivity estimation
Rdf conjunctive query selectivity estimation
 
Plreuse
PlreusePlreuse
Plreuse
 
Paxquery
PaxqueryPaxquery
Paxquery
 
Conjunctive queries
Conjunctive queriesConjunctive queries
Conjunctive queries
 
CliqueSquare processing
CliqueSquare processingCliqueSquare processing
CliqueSquare processing
 

Dernier

FESE Capital Markets Fact Sheet 2024 Q1.pdf
FESE Capital Markets Fact Sheet 2024 Q1.pdfFESE Capital Markets Fact Sheet 2024 Q1.pdf
FESE Capital Markets Fact Sheet 2024 Q1.pdfMarinCaroMartnezBerg
 
VIP Call Girls Service Miyapur Hyderabad Call +91-8250192130
VIP Call Girls Service Miyapur Hyderabad Call +91-8250192130VIP Call Girls Service Miyapur Hyderabad Call +91-8250192130
VIP Call Girls Service Miyapur Hyderabad Call +91-8250192130Suhani Kapoor
 
꧁❤ Greater Noida Call Girls Delhi ❤꧂ 9711199171 ☎️ Hard And Sexy Vip Call
꧁❤ Greater Noida Call Girls Delhi ❤꧂ 9711199171 ☎️ Hard And Sexy Vip Call꧁❤ Greater Noida Call Girls Delhi ❤꧂ 9711199171 ☎️ Hard And Sexy Vip Call
꧁❤ Greater Noida Call Girls Delhi ❤꧂ 9711199171 ☎️ Hard And Sexy Vip Callshivangimorya083
 
BPAC WITH UFSBI GENERAL PRESENTATION 18_05_2017-1.pptx
BPAC WITH UFSBI GENERAL PRESENTATION 18_05_2017-1.pptxBPAC WITH UFSBI GENERAL PRESENTATION 18_05_2017-1.pptx
BPAC WITH UFSBI GENERAL PRESENTATION 18_05_2017-1.pptxMohammedJunaid861692
 
(PARI) Call Girls Wanowrie ( 7001035870 ) HI-Fi Pune Escorts Service
(PARI) Call Girls Wanowrie ( 7001035870 ) HI-Fi Pune Escorts Service(PARI) Call Girls Wanowrie ( 7001035870 ) HI-Fi Pune Escorts Service
(PARI) Call Girls Wanowrie ( 7001035870 ) HI-Fi Pune Escorts Serviceranjana rawat
 
Brighton SEO | April 2024 | Data Storytelling
Brighton SEO | April 2024 | Data StorytellingBrighton SEO | April 2024 | Data Storytelling
Brighton SEO | April 2024 | Data StorytellingNeil Barnes
 
Log Analysis using OSSEC sasoasasasas.pptx
Log Analysis using OSSEC sasoasasasas.pptxLog Analysis using OSSEC sasoasasasas.pptx
Log Analysis using OSSEC sasoasasasas.pptxJohnnyPlasten
 
Market Analysis in the 5 Largest Economic Countries in Southeast Asia.pdf
Market Analysis in the 5 Largest Economic Countries in Southeast Asia.pdfMarket Analysis in the 5 Largest Economic Countries in Southeast Asia.pdf
Market Analysis in the 5 Largest Economic Countries in Southeast Asia.pdfRachmat Ramadhan H
 
Industrialised data - the key to AI success.pdf
Industrialised data - the key to AI success.pdfIndustrialised data - the key to AI success.pdf
Industrialised data - the key to AI success.pdfLars Albertsson
 
Halmar dropshipping via API with DroFx
Halmar  dropshipping  via API with DroFxHalmar  dropshipping  via API with DroFx
Halmar dropshipping via API with DroFxolyaivanovalion
 
Carero dropshipping via API with DroFx.pptx
Carero dropshipping via API with DroFx.pptxCarero dropshipping via API with DroFx.pptx
Carero dropshipping via API with DroFx.pptxolyaivanovalion
 
B2 Creative Industry Response Evaluation.docx
B2 Creative Industry Response Evaluation.docxB2 Creative Industry Response Evaluation.docx
B2 Creative Industry Response Evaluation.docxStephen266013
 
Generative AI on Enterprise Cloud with NiFi and Milvus
Generative AI on Enterprise Cloud with NiFi and MilvusGenerative AI on Enterprise Cloud with NiFi and Milvus
Generative AI on Enterprise Cloud with NiFi and MilvusTimothy Spann
 
Delhi Call Girls CP 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip Call
Delhi Call Girls CP 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip CallDelhi Call Girls CP 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip Call
Delhi Call Girls CP 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip Callshivangimorya083
 
Week-01-2.ppt BBB human Computer interaction
Week-01-2.ppt BBB human Computer interactionWeek-01-2.ppt BBB human Computer interaction
Week-01-2.ppt BBB human Computer interactionfulawalesam
 
April 2024 - Crypto Market Report's Analysis
April 2024 - Crypto Market Report's AnalysisApril 2024 - Crypto Market Report's Analysis
April 2024 - Crypto Market Report's Analysismanisha194592
 
CebaBaby dropshipping via API with DroFX.pptx
CebaBaby dropshipping via API with DroFX.pptxCebaBaby dropshipping via API with DroFX.pptx
CebaBaby dropshipping via API with DroFX.pptxolyaivanovalion
 

Dernier (20)

FESE Capital Markets Fact Sheet 2024 Q1.pdf
FESE Capital Markets Fact Sheet 2024 Q1.pdfFESE Capital Markets Fact Sheet 2024 Q1.pdf
FESE Capital Markets Fact Sheet 2024 Q1.pdf
 
VIP Call Girls Service Miyapur Hyderabad Call +91-8250192130
VIP Call Girls Service Miyapur Hyderabad Call +91-8250192130VIP Call Girls Service Miyapur Hyderabad Call +91-8250192130
VIP Call Girls Service Miyapur Hyderabad Call +91-8250192130
 
꧁❤ Greater Noida Call Girls Delhi ❤꧂ 9711199171 ☎️ Hard And Sexy Vip Call
꧁❤ Greater Noida Call Girls Delhi ❤꧂ 9711199171 ☎️ Hard And Sexy Vip Call꧁❤ Greater Noida Call Girls Delhi ❤꧂ 9711199171 ☎️ Hard And Sexy Vip Call
꧁❤ Greater Noida Call Girls Delhi ❤꧂ 9711199171 ☎️ Hard And Sexy Vip Call
 
BPAC WITH UFSBI GENERAL PRESENTATION 18_05_2017-1.pptx
BPAC WITH UFSBI GENERAL PRESENTATION 18_05_2017-1.pptxBPAC WITH UFSBI GENERAL PRESENTATION 18_05_2017-1.pptx
BPAC WITH UFSBI GENERAL PRESENTATION 18_05_2017-1.pptx
 
(PARI) Call Girls Wanowrie ( 7001035870 ) HI-Fi Pune Escorts Service
(PARI) Call Girls Wanowrie ( 7001035870 ) HI-Fi Pune Escorts Service(PARI) Call Girls Wanowrie ( 7001035870 ) HI-Fi Pune Escorts Service
(PARI) Call Girls Wanowrie ( 7001035870 ) HI-Fi Pune Escorts Service
 
Brighton SEO | April 2024 | Data Storytelling
Brighton SEO | April 2024 | Data StorytellingBrighton SEO | April 2024 | Data Storytelling
Brighton SEO | April 2024 | Data Storytelling
 
VIP Call Girls Service Charbagh { Lucknow Call Girls Service 9548273370 } Boo...
VIP Call Girls Service Charbagh { Lucknow Call Girls Service 9548273370 } Boo...VIP Call Girls Service Charbagh { Lucknow Call Girls Service 9548273370 } Boo...
VIP Call Girls Service Charbagh { Lucknow Call Girls Service 9548273370 } Boo...
 
Log Analysis using OSSEC sasoasasasas.pptx
Log Analysis using OSSEC sasoasasasas.pptxLog Analysis using OSSEC sasoasasasas.pptx
Log Analysis using OSSEC sasoasasasas.pptx
 
Market Analysis in the 5 Largest Economic Countries in Southeast Asia.pdf
Market Analysis in the 5 Largest Economic Countries in Southeast Asia.pdfMarket Analysis in the 5 Largest Economic Countries in Southeast Asia.pdf
Market Analysis in the 5 Largest Economic Countries in Southeast Asia.pdf
 
Industrialised data - the key to AI success.pdf
Industrialised data - the key to AI success.pdfIndustrialised data - the key to AI success.pdf
Industrialised data - the key to AI success.pdf
 
Halmar dropshipping via API with DroFx
Halmar  dropshipping  via API with DroFxHalmar  dropshipping  via API with DroFx
Halmar dropshipping via API with DroFx
 
Carero dropshipping via API with DroFx.pptx
Carero dropshipping via API with DroFx.pptxCarero dropshipping via API with DroFx.pptx
Carero dropshipping via API with DroFx.pptx
 
B2 Creative Industry Response Evaluation.docx
B2 Creative Industry Response Evaluation.docxB2 Creative Industry Response Evaluation.docx
B2 Creative Industry Response Evaluation.docx
 
Generative AI on Enterprise Cloud with NiFi and Milvus
Generative AI on Enterprise Cloud with NiFi and MilvusGenerative AI on Enterprise Cloud with NiFi and Milvus
Generative AI on Enterprise Cloud with NiFi and Milvus
 
Delhi 99530 vip 56974 Genuine Escort Service Call Girls in Kishangarh
Delhi 99530 vip 56974 Genuine Escort Service Call Girls in  KishangarhDelhi 99530 vip 56974 Genuine Escort Service Call Girls in  Kishangarh
Delhi 99530 vip 56974 Genuine Escort Service Call Girls in Kishangarh
 
Delhi Call Girls CP 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip Call
Delhi Call Girls CP 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip CallDelhi Call Girls CP 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip Call
Delhi Call Girls CP 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip Call
 
Week-01-2.ppt BBB human Computer interaction
Week-01-2.ppt BBB human Computer interactionWeek-01-2.ppt BBB human Computer interaction
Week-01-2.ppt BBB human Computer interaction
 
꧁❤ Aerocity Call Girls Service Aerocity Delhi ❤꧂ 9999965857 ☎️ Hard And Sexy ...
꧁❤ Aerocity Call Girls Service Aerocity Delhi ❤꧂ 9999965857 ☎️ Hard And Sexy ...꧁❤ Aerocity Call Girls Service Aerocity Delhi ❤꧂ 9999965857 ☎️ Hard And Sexy ...
꧁❤ Aerocity Call Girls Service Aerocity Delhi ❤꧂ 9999965857 ☎️ Hard And Sexy ...
 
April 2024 - Crypto Market Report's Analysis
April 2024 - Crypto Market Report's AnalysisApril 2024 - Crypto Market Report's Analysis
April 2024 - Crypto Market Report's Analysis
 
CebaBaby dropshipping via API with DroFX.pptx
CebaBaby dropshipping via API with DroFX.pptxCebaBaby dropshipping via API with DroFX.pptx
CebaBaby dropshipping via API with DroFX.pptx
 

rdf query reformulation

  • 1. OAK Code Days: RDF query reformulation algorithm OAK Code Days: RDF query reformulation algorithm owner: Damian Bursztyn presenter: Raphaël Bonaque September 16th 2014 1 / 9
  • 2. OAK Code Days: RDF query reformulation algorithm What does it do ? What Rewrite a certain class of queries into a list of queries equivalent in different settings: Input: a conjunctive RDF query an RDF Schema Output: a union of conjunctive queries such that Q0(D) = Q(Dinf ) i.e. it is equivalent, on a set of data triples, with the original query on the saturation of this triples with the RDF Schema 2 / 9
  • 3. OAK Code Days: RDF query reformulation algorithm What does it do ? The code All in JAVA Code history: First monolithic version by Konstantinos and Julian Modular rewriting by Damian Performance improvements by Damian Location: on GForge at https://scm.gforge.inria.fr/ svn/distriples/trunk/Reformulator/ source code unitary tests total number of lines 2722 1272 3994 number of classes 30 15 45 3 / 9
  • 4. OAK Code Days: RDF query reformulation algorithm Who is dealing with the code ? Who Wrote it: Damian Julian Konstantinos Is using it: Damian (Alexandra, with an old version, in Sat. versus Ref.) 4 / 9
  • 5. OAK Code Days: RDF query reformulation algorithm How does it work ? General overview How: overview 6 packages: reasoning: the general entry point of the code, contains: schema: models RDF Schemas a reasoner a much faster concurrent version of reasoner (up to 12.5 times faster) rules: contains the rewriting rules rulesets: basically a collection of rules (maintained for compatibility) conjunctive query signature: some queries are equivalent we want to avoid duplicates Equivalence hard to compute (NP), signature easier, less powerfull (all queries with same signature are equivalent, some equivalent queries have different signatures) exceptions utilities: some minor utilities 5 / 9
  • 6. OAK Code Days: RDF query reformulation algorithm How does it work ? Libraries How: libraries Jena: used for instance for constants like http://www.w3.org/1999/02/22-rdf-syntax-ns#type and to model the schema Xerces: for parsing RDF schema (Requieres icu and iri) 2 logging libraries: log4j the classical logger used by the team slf4j to log Xerces 6 / 9
  • 7. OAK Code Days: RDF query reformulation algorithm Litterature Which papers View Selection in Semantic Web Databases by François Goasdoué, Konstantinos Karanasos, Julien Leblay and Ioana Manolescu Efficient Query Answering against Dynamic RDF Databases by François Goasdoué, Ioana Manolescu, Alexandra Roatis 7 / 9
  • 8. OAK Code Days: RDF query reformulation algorithm Next things to do TODOs & bugs No known bug Create a test that checks the content of a reformulation, not only its size 8 / 9
  • 9. OAK Code Days: RDF query reformulation algorithm Conclusion Questions ? Thank you for listening ! 9 / 9