SlideShare a Scribd company logo

Enterprise knowledge graphs

Presentation from Semantics 2016

1 of 49
Download to read offline
Enterprise Knowledge Graphs
Sören Auer
https://www.eccenca.com
The three Big Data „V“ – Variety is often neglected
Quelle: Gesellschaft für Informatik
Sören Auer 2
Linked Data Principles
Addressing the neglected third V (Variety)
1. Use URIs to identify the “things” in your data
2. Use http:// URIs so people (and machines) can
look them up on the web
3. When a URI is looked up, return a description of
the thing (in RDF format)
4. Include links to related things
http://www.w3.org/DesignIssues/LinkedData.html
3
[1] Auer, Lehmann, Ngomo, Zaveri: Introduction to Linked Data and Its Lifecycle on the Web. Reasoning Web 2013
Linked (Open) Data: The RDF Data Model
4
RDF = Resource Description Framework
located in
label
industry
headquarters
full nameDHL
Post Tower
162.5 m
Bonn
Logistics Logistik
DHL International GmbH
height
物流
label
Sören Auer
RDF Data Model (a bit more technical)
– Graph consists of:
• Resources (identified via URIs)
• Literals: data values with data type (URI) or language (multilinguality integrated)
• Attributes of resources are also URI-identified (from vocabularies)
– Various data sources and vocabularies can be arbitrarily mixed and meshed
– URIs can be shortened with namespace prefixes; e.g. dbp: → http://dbpedia.org/resource/
gn:locatedIn
rdfs:label
dbo:industry
ex:headquarters
foaf:namedbp:DHL_International_GmbH
dbp:Post_Tower
"162.5"^^xsd:decimal
dbp:Bonn
dbp:Logistics
"Logistik"@de
"DHL International GmbH"^^xsd:string
ex:height
"物流"@zh
rdfs:label
rdf:value
unit:Meter
ex:unit
RDF mediates between different Data Models &
bridges between Conceptual and Operational Layers
Id Title Screen
5624 SmartTV 104cm
5627 Tablet 21cm
Prod:5624 rdf:type Electronics
Prod:5624 rdfs:label “SmartTV”
Prod:5624 hasScreenSize “104”^^unit:cm
...
Electronics
Vehicle
Car Bus Truck
Vehicle rdf:type owl:Thing
Car rdfs:subClassOf Vehicle
Bus rdfs:subClassOf Vehicle
...
Tabular/Relational Data
Taxonomic/Tree Data
Logical Axioms / Schema
Male rdfs:subClassOf Human
Female rdfs:subClassOf Human
Male owl:disjointWith Female
...
Sören Auer 6
Ad

Recommended

Linked data for Enterprise Data Integration
Linked data for Enterprise Data IntegrationLinked data for Enterprise Data Integration
Linked data for Enterprise Data IntegrationSören Auer
 
LDOW2015 Position Talk and Discussion
LDOW2015 Position Talk and DiscussionLDOW2015 Position Talk and Discussion
LDOW2015 Position Talk and DiscussionSören Auer
 
Towards digitizing scholarly communication
Towards digitizing scholarly communicationTowards digitizing scholarly communication
Towards digitizing scholarly communicationSören Auer
 
What can linked data do for digital libraries
What can linked data do for digital librariesWhat can linked data do for digital libraries
What can linked data do for digital librariesSören Auer
 
Introduction to the Data Web, DBpedia and the Life-cycle of Linked Data
Introduction to the Data Web, DBpedia and the Life-cycle of Linked DataIntroduction to the Data Web, DBpedia and the Life-cycle of Linked Data
Introduction to the Data Web, DBpedia and the Life-cycle of Linked DataSören Auer
 
Knowledge Graph Introduction
Knowledge Graph IntroductionKnowledge Graph Introduction
Knowledge Graph IntroductionSören Auer
 
Towards an Open Research Knowledge Graph
Towards an Open Research Knowledge GraphTowards an Open Research Knowledge Graph
Towards an Open Research Knowledge GraphSören Auer
 

More Related Content

What's hot

Das Semantische Daten Web für Unternehmen
Das Semantische Daten Web für UnternehmenDas Semantische Daten Web für Unternehmen
Das Semantische Daten Web für UnternehmenSören Auer
 
The Bounties of Semantic Data Integration for the Enterprise
The Bounties of Semantic Data Integration for the Enterprise The Bounties of Semantic Data Integration for the Enterprise
The Bounties of Semantic Data Integration for the Enterprise Ontotext
 
Linking Open, Big Data Using Semantic Web Technologies - An Introduction
Linking Open, Big Data Using Semantic Web Technologies - An IntroductionLinking Open, Big Data Using Semantic Web Technologies - An Introduction
Linking Open, Big Data Using Semantic Web Technologies - An IntroductionRonald Ashri
 
Using the Semantic Web Stack to Make Big Data Smarter
Using the Semantic Web Stack to Make  Big Data SmarterUsing the Semantic Web Stack to Make  Big Data Smarter
Using the Semantic Web Stack to Make Big Data SmarterMatheus Mota
 
Getting Started with Knowledge Graphs
Getting Started with Knowledge GraphsGetting Started with Knowledge Graphs
Getting Started with Knowledge GraphsPeter Haase
 
Knowledge graphs on the Web
Knowledge graphs on the WebKnowledge graphs on the Web
Knowledge graphs on the WebArmin Haller
 
Build Narratives, Connect Artifacts: Linked Open Data for Cultural Heritage
Build Narratives, Connect Artifacts: Linked Open Data for Cultural HeritageBuild Narratives, Connect Artifacts: Linked Open Data for Cultural Heritage
Build Narratives, Connect Artifacts: Linked Open Data for Cultural HeritageOntotext
 
Smart Data Applications powered by the Wikidata Knowledge Graph
Smart Data Applications powered by the Wikidata Knowledge GraphSmart Data Applications powered by the Wikidata Knowledge Graph
Smart Data Applications powered by the Wikidata Knowledge GraphPeter Haase
 
Linked Data Experiences at Springer Nature
Linked Data Experiences at Springer NatureLinked Data Experiences at Springer Nature
Linked Data Experiences at Springer NatureMichele Pasin
 
From Open Linked Data towards an Ecosystem of Interlinked Knowledge
From Open Linked Data towards an Ecosystem of Interlinked KnowledgeFrom Open Linked Data towards an Ecosystem of Interlinked Knowledge
From Open Linked Data towards an Ecosystem of Interlinked KnowledgeSören Auer
 
ESWC 2017 Tutorial Knowledge Graphs
ESWC 2017 Tutorial Knowledge GraphsESWC 2017 Tutorial Knowledge Graphs
ESWC 2017 Tutorial Knowledge GraphsPeter Haase
 
The Power of Semantic Technologies to Explore Linked Open Data
The Power of Semantic Technologies to Explore Linked Open DataThe Power of Semantic Technologies to Explore Linked Open Data
The Power of Semantic Technologies to Explore Linked Open DataOntotext
 
First Steps in Semantic Data Modelling and Search & Analytics in the Cloud
First Steps in Semantic Data Modelling and Search & Analytics in the CloudFirst Steps in Semantic Data Modelling and Search & Analytics in the Cloud
First Steps in Semantic Data Modelling and Search & Analytics in the CloudOntotext
 
Nicoletta Fornara and Fabio Marfia | Modeling and Enforcing Access Control Ob...
Nicoletta Fornara and Fabio Marfia | Modeling and Enforcing Access Control Ob...Nicoletta Fornara and Fabio Marfia | Modeling and Enforcing Access Control Ob...
Nicoletta Fornara and Fabio Marfia | Modeling and Enforcing Access Control Ob...semanticsconference
 
Creating knowledge out of interlinked data
Creating knowledge out of interlinked dataCreating knowledge out of interlinked data
Creating knowledge out of interlinked dataSören Auer
 
Scalable and privacy-preserving data integration - part 1
Scalable and privacy-preserving data integration - part 1Scalable and privacy-preserving data integration - part 1
Scalable and privacy-preserving data integration - part 1ErhardRahm
 
Ephedra: efficiently combining RDF data and services using SPARQL federation
Ephedra: efficiently combining RDF data and services using SPARQL federationEphedra: efficiently combining RDF data and services using SPARQL federation
Ephedra: efficiently combining RDF data and services using SPARQL federationPeter Haase
 
Analytics on Big Knowledge Graphs Deliver Entity Awareness and Help Data Linking
Analytics on Big Knowledge Graphs Deliver Entity Awareness and Help Data LinkingAnalytics on Big Knowledge Graphs Deliver Entity Awareness and Help Data Linking
Analytics on Big Knowledge Graphs Deliver Entity Awareness and Help Data LinkingOntotext
 
Towards Knowledge Graph based Representation, Augmentation and Exploration of...
Towards Knowledge Graph based Representation, Augmentation and Exploration of...Towards Knowledge Graph based Representation, Augmentation and Exploration of...
Towards Knowledge Graph based Representation, Augmentation and Exploration of...Sören Auer
 

What's hot (20)

Das Semantische Daten Web für Unternehmen
Das Semantische Daten Web für UnternehmenDas Semantische Daten Web für Unternehmen
Das Semantische Daten Web für Unternehmen
 
The Bounties of Semantic Data Integration for the Enterprise
The Bounties of Semantic Data Integration for the Enterprise The Bounties of Semantic Data Integration for the Enterprise
The Bounties of Semantic Data Integration for the Enterprise
 
Linking Open, Big Data Using Semantic Web Technologies - An Introduction
Linking Open, Big Data Using Semantic Web Technologies - An IntroductionLinking Open, Big Data Using Semantic Web Technologies - An Introduction
Linking Open, Big Data Using Semantic Web Technologies - An Introduction
 
Using the Semantic Web Stack to Make Big Data Smarter
Using the Semantic Web Stack to Make  Big Data SmarterUsing the Semantic Web Stack to Make  Big Data Smarter
Using the Semantic Web Stack to Make Big Data Smarter
 
Getting Started with Knowledge Graphs
Getting Started with Knowledge GraphsGetting Started with Knowledge Graphs
Getting Started with Knowledge Graphs
 
Knowledge graphs on the Web
Knowledge graphs on the WebKnowledge graphs on the Web
Knowledge graphs on the Web
 
Build Narratives, Connect Artifacts: Linked Open Data for Cultural Heritage
Build Narratives, Connect Artifacts: Linked Open Data for Cultural HeritageBuild Narratives, Connect Artifacts: Linked Open Data for Cultural Heritage
Build Narratives, Connect Artifacts: Linked Open Data for Cultural Heritage
 
Smart Data Applications powered by the Wikidata Knowledge Graph
Smart Data Applications powered by the Wikidata Knowledge GraphSmart Data Applications powered by the Wikidata Knowledge Graph
Smart Data Applications powered by the Wikidata Knowledge Graph
 
Linked Data Experiences at Springer Nature
Linked Data Experiences at Springer NatureLinked Data Experiences at Springer Nature
Linked Data Experiences at Springer Nature
 
From Open Linked Data towards an Ecosystem of Interlinked Knowledge
From Open Linked Data towards an Ecosystem of Interlinked KnowledgeFrom Open Linked Data towards an Ecosystem of Interlinked Knowledge
From Open Linked Data towards an Ecosystem of Interlinked Knowledge
 
ESWC 2017 Tutorial Knowledge Graphs
ESWC 2017 Tutorial Knowledge GraphsESWC 2017 Tutorial Knowledge Graphs
ESWC 2017 Tutorial Knowledge Graphs
 
The Power of Semantic Technologies to Explore Linked Open Data
The Power of Semantic Technologies to Explore Linked Open DataThe Power of Semantic Technologies to Explore Linked Open Data
The Power of Semantic Technologies to Explore Linked Open Data
 
First Steps in Semantic Data Modelling and Search & Analytics in the Cloud
First Steps in Semantic Data Modelling and Search & Analytics in the CloudFirst Steps in Semantic Data Modelling and Search & Analytics in the Cloud
First Steps in Semantic Data Modelling and Search & Analytics in the Cloud
 
Nicoletta Fornara and Fabio Marfia | Modeling and Enforcing Access Control Ob...
Nicoletta Fornara and Fabio Marfia | Modeling and Enforcing Access Control Ob...Nicoletta Fornara and Fabio Marfia | Modeling and Enforcing Access Control Ob...
Nicoletta Fornara and Fabio Marfia | Modeling and Enforcing Access Control Ob...
 
Creating knowledge out of interlinked data
Creating knowledge out of interlinked dataCreating knowledge out of interlinked data
Creating knowledge out of interlinked data
 
Scalable and privacy-preserving data integration - part 1
Scalable and privacy-preserving data integration - part 1Scalable and privacy-preserving data integration - part 1
Scalable and privacy-preserving data integration - part 1
 
Ephedra: efficiently combining RDF data and services using SPARQL federation
Ephedra: efficiently combining RDF data and services using SPARQL federationEphedra: efficiently combining RDF data and services using SPARQL federation
Ephedra: efficiently combining RDF data and services using SPARQL federation
 
Analytics on Big Knowledge Graphs Deliver Entity Awareness and Help Data Linking
Analytics on Big Knowledge Graphs Deliver Entity Awareness and Help Data LinkingAnalytics on Big Knowledge Graphs Deliver Entity Awareness and Help Data Linking
Analytics on Big Knowledge Graphs Deliver Entity Awareness and Help Data Linking
 
DBPedia-past-present-future
DBPedia-past-present-futureDBPedia-past-present-future
DBPedia-past-present-future
 
Towards Knowledge Graph based Representation, Augmentation and Exploration of...
Towards Knowledge Graph based Representation, Augmentation and Exploration of...Towards Knowledge Graph based Representation, Augmentation and Exploration of...
Towards Knowledge Graph based Representation, Augmentation and Exploration of...
 

Similar to Enterprise knowledge graphs

Sören Auer | Enterprise Knowledge Graphs
Sören Auer | Enterprise Knowledge GraphsSören Auer | Enterprise Knowledge Graphs
Sören Auer | Enterprise Knowledge Graphssemanticsconference
 
Linked data HHS 2015
Linked data HHS 2015Linked data HHS 2015
Linked data HHS 2015Cason Snow
 
Scaling up Linked Data
Scaling up Linked DataScaling up Linked Data
Scaling up Linked DataMarin Dimitrov
 
Structured Dynamics' Semantic Technologies Product Stack
Structured Dynamics' Semantic Technologies Product StackStructured Dynamics' Semantic Technologies Product Stack
Structured Dynamics' Semantic Technologies Product StackMike Bergman
 
Linked Data Tutorial
Linked Data TutorialLinked Data Tutorial
Linked Data TutorialSören Auer
 
Why I don't use Semantic Web technologies anymore, event if they still influe...
Why I don't use Semantic Web technologies anymore, event if they still influe...Why I don't use Semantic Web technologies anymore, event if they still influe...
Why I don't use Semantic Web technologies anymore, event if they still influe...Gautier Poupeau
 
Linked Data Driven Data Virtualization for Web-scale Integration
Linked Data Driven Data Virtualization for Web-scale IntegrationLinked Data Driven Data Virtualization for Web-scale Integration
Linked Data Driven Data Virtualization for Web-scale Integrationrumito
 
RDF-Gen: Generating RDF from streaming and archival data
RDF-Gen: Generating RDF from streaming and archival dataRDF-Gen: Generating RDF from streaming and archival data
RDF-Gen: Generating RDF from streaming and archival dataGiorgos Santipantakis
 
State of the Semantic Web
State of the Semantic WebState of the Semantic Web
State of the Semantic WebIvan Herman
 
RDF Graph Data Management in Oracle Database and NoSQL Platforms
RDF Graph Data Management in Oracle Database and NoSQL PlatformsRDF Graph Data Management in Oracle Database and NoSQL Platforms
RDF Graph Data Management in Oracle Database and NoSQL PlatformsGraph-TA
 
Scaling up Linked Data
Scaling up Linked DataScaling up Linked Data
Scaling up Linked DataEUCLID project
 
Introduction to linked data
Introduction to linked dataIntroduction to linked data
Introduction to linked dataLaura Po
 
Usage of Linked Data: Introduction and Application Scenarios
Usage of Linked Data: Introduction and Application ScenariosUsage of Linked Data: Introduction and Application Scenarios
Usage of Linked Data: Introduction and Application ScenariosEUCLID project
 
Vital AI: Big Data Modeling
Vital AI: Big Data ModelingVital AI: Big Data Modeling
Vital AI: Big Data ModelingVital.AI
 
FIWARE Global Summit - IDS Implementation with FIWARE Software Components
FIWARE Global Summit - IDS Implementation with FIWARE Software ComponentsFIWARE Global Summit - IDS Implementation with FIWARE Software Components
FIWARE Global Summit - IDS Implementation with FIWARE Software ComponentsFIWARE
 
Web 3 Mark Greaves
Web 3 Mark GreavesWeb 3 Mark Greaves
Web 3 Mark GreavesMediabistro
 
Web 3.0 & IoT (English)
Web 3.0 & IoT (English)Web 3.0 & IoT (English)
Web 3.0 & IoT (English)Peter Waher
 
Web 3.0 & io t (en)
Web 3.0 & io t (en)Web 3.0 & io t (en)
Web 3.0 & io t (en)Rikard Strid
 
Linked data demystified:Practical efforts to transform CONTENTDM metadata int...
Linked data demystified:Practical efforts to transform CONTENTDM metadata int...Linked data demystified:Practical efforts to transform CONTENTDM metadata int...
Linked data demystified:Practical efforts to transform CONTENTDM metadata int...Cory Lampert
 

Similar to Enterprise knowledge graphs (20)

Sören Auer | Enterprise Knowledge Graphs
Sören Auer | Enterprise Knowledge GraphsSören Auer | Enterprise Knowledge Graphs
Sören Auer | Enterprise Knowledge Graphs
 
Linked data HHS 2015
Linked data HHS 2015Linked data HHS 2015
Linked data HHS 2015
 
Scaling up Linked Data
Scaling up Linked DataScaling up Linked Data
Scaling up Linked Data
 
Structured Dynamics' Semantic Technologies Product Stack
Structured Dynamics' Semantic Technologies Product StackStructured Dynamics' Semantic Technologies Product Stack
Structured Dynamics' Semantic Technologies Product Stack
 
Linked Data Tutorial
Linked Data TutorialLinked Data Tutorial
Linked Data Tutorial
 
Why I don't use Semantic Web technologies anymore, event if they still influe...
Why I don't use Semantic Web technologies anymore, event if they still influe...Why I don't use Semantic Web technologies anymore, event if they still influe...
Why I don't use Semantic Web technologies anymore, event if they still influe...
 
Linked Data Driven Data Virtualization for Web-scale Integration
Linked Data Driven Data Virtualization for Web-scale IntegrationLinked Data Driven Data Virtualization for Web-scale Integration
Linked Data Driven Data Virtualization for Web-scale Integration
 
RDF-Gen: Generating RDF from streaming and archival data
RDF-Gen: Generating RDF from streaming and archival dataRDF-Gen: Generating RDF from streaming and archival data
RDF-Gen: Generating RDF from streaming and archival data
 
State of the Semantic Web
State of the Semantic WebState of the Semantic Web
State of the Semantic Web
 
RDF Graph Data Management in Oracle Database and NoSQL Platforms
RDF Graph Data Management in Oracle Database and NoSQL PlatformsRDF Graph Data Management in Oracle Database and NoSQL Platforms
RDF Graph Data Management in Oracle Database and NoSQL Platforms
 
Scaling up Linked Data
Scaling up Linked DataScaling up Linked Data
Scaling up Linked Data
 
Introduction to linked data
Introduction to linked dataIntroduction to linked data
Introduction to linked data
 
Usage of Linked Data: Introduction and Application Scenarios
Usage of Linked Data: Introduction and Application ScenariosUsage of Linked Data: Introduction and Application Scenarios
Usage of Linked Data: Introduction and Application Scenarios
 
Vital AI: Big Data Modeling
Vital AI: Big Data ModelingVital AI: Big Data Modeling
Vital AI: Big Data Modeling
 
FIWARE Global Summit - IDS Implementation with FIWARE Software Components
FIWARE Global Summit - IDS Implementation with FIWARE Software ComponentsFIWARE Global Summit - IDS Implementation with FIWARE Software Components
FIWARE Global Summit - IDS Implementation with FIWARE Software Components
 
Web 3 Mark Greaves
Web 3 Mark GreavesWeb 3 Mark Greaves
Web 3 Mark Greaves
 
Web 3.0 & IoT (English)
Web 3.0 & IoT (English)Web 3.0 & IoT (English)
Web 3.0 & IoT (English)
 
Web 3.0 & io t (en)
Web 3.0 & io t (en)Web 3.0 & io t (en)
Web 3.0 & io t (en)
 
Linked data demystified:Practical efforts to transform CONTENTDM metadata int...
Linked data demystified:Practical efforts to transform CONTENTDM metadata int...Linked data demystified:Practical efforts to transform CONTENTDM metadata int...
Linked data demystified:Practical efforts to transform CONTENTDM metadata int...
 
Semantic web
Semantic web Semantic web
Semantic web
 

More from Sören Auer

Knowledge Graph Research and Innovation Challenges
Knowledge Graph Research and Innovation ChallengesKnowledge Graph Research and Innovation Challenges
Knowledge Graph Research and Innovation ChallengesSören Auer
 
Describing Scholarly Contributions semantically with the Open Research Knowle...
Describing Scholarly Contributions semantically with the Open Research Knowle...Describing Scholarly Contributions semantically with the Open Research Knowle...
Describing Scholarly Contributions semantically with the Open Research Knowle...Sören Auer
 
DBpedia - 10 year ISWC SWSA best paper award presentation
DBpedia  - 10 year ISWC SWSA best paper award presentationDBpedia  - 10 year ISWC SWSA best paper award presentation
DBpedia - 10 year ISWC SWSA best paper award presentationSören Auer
 
Project overview big data europe
Project overview big data europeProject overview big data europe
Project overview big data europeSören Auer
 
Open data for smart cities
Open data for smart citiesOpen data for smart cities
Open data for smart citiesSören Auer
 
The web of interlinked data and knowledge stripped
The web of interlinked data and knowledge strippedThe web of interlinked data and knowledge stripped
The web of interlinked data and knowledge strippedSören Auer
 
Проект Евросоюза LOD2 и Британский Институт Открытых данных
Проект Евросоюза LOD2 и Британский Институт Открытых данныхПроект Евросоюза LOD2 и Британский Институт Открытых данных
Проект Евросоюза LOD2 и Британский Институт Открытых данныхSören Auer
 
Linked data and semantic wikis
Linked data and semantic wikisLinked data and semantic wikis
Linked data and semantic wikisSören Auer
 
ESWC2010 "Linked Data: Now what?" Panel Discussion slides
ESWC2010 "Linked Data: Now what?" Panel Discussion slidesESWC2010 "Linked Data: Now what?" Panel Discussion slides
ESWC2010 "Linked Data: Now what?" Panel Discussion slidesSören Auer
 
LESS - Template-based Syndication and Presentation of Linked Data for End-users
LESS - Template-based Syndication and Presentation of Linked Data for End-usersLESS - Template-based Syndication and Presentation of Linked Data for End-users
LESS - Template-based Syndication and Presentation of Linked Data for End-usersSören Auer
 
Overview AG AKSW
Overview AG AKSWOverview AG AKSW
Overview AG AKSWSören Auer
 
WWW09 - Triplify Light-Weight Linked Data Publication from Relational Databases
WWW09 - Triplify Light-Weight Linked Data Publication from Relational DatabasesWWW09 - Triplify Light-Weight Linked Data Publication from Relational Databases
WWW09 - Triplify Light-Weight Linked Data Publication from Relational DatabasesSören Auer
 
Participatory Research
Participatory ResearchParticipatory Research
Participatory ResearchSören Auer
 

More from Sören Auer (13)

Knowledge Graph Research and Innovation Challenges
Knowledge Graph Research and Innovation ChallengesKnowledge Graph Research and Innovation Challenges
Knowledge Graph Research and Innovation Challenges
 
Describing Scholarly Contributions semantically with the Open Research Knowle...
Describing Scholarly Contributions semantically with the Open Research Knowle...Describing Scholarly Contributions semantically with the Open Research Knowle...
Describing Scholarly Contributions semantically with the Open Research Knowle...
 
DBpedia - 10 year ISWC SWSA best paper award presentation
DBpedia  - 10 year ISWC SWSA best paper award presentationDBpedia  - 10 year ISWC SWSA best paper award presentation
DBpedia - 10 year ISWC SWSA best paper award presentation
 
Project overview big data europe
Project overview big data europeProject overview big data europe
Project overview big data europe
 
Open data for smart cities
Open data for smart citiesOpen data for smart cities
Open data for smart cities
 
The web of interlinked data and knowledge stripped
The web of interlinked data and knowledge strippedThe web of interlinked data and knowledge stripped
The web of interlinked data and knowledge stripped
 
Проект Евросоюза LOD2 и Британский Институт Открытых данных
Проект Евросоюза LOD2 и Британский Институт Открытых данныхПроект Евросоюза LOD2 и Британский Институт Открытых данных
Проект Евросоюза LOD2 и Британский Институт Открытых данных
 
Linked data and semantic wikis
Linked data and semantic wikisLinked data and semantic wikis
Linked data and semantic wikis
 
ESWC2010 "Linked Data: Now what?" Panel Discussion slides
ESWC2010 "Linked Data: Now what?" Panel Discussion slidesESWC2010 "Linked Data: Now what?" Panel Discussion slides
ESWC2010 "Linked Data: Now what?" Panel Discussion slides
 
LESS - Template-based Syndication and Presentation of Linked Data for End-users
LESS - Template-based Syndication and Presentation of Linked Data for End-usersLESS - Template-based Syndication and Presentation of Linked Data for End-users
LESS - Template-based Syndication and Presentation of Linked Data for End-users
 
Overview AG AKSW
Overview AG AKSWOverview AG AKSW
Overview AG AKSW
 
WWW09 - Triplify Light-Weight Linked Data Publication from Relational Databases
WWW09 - Triplify Light-Weight Linked Data Publication from Relational DatabasesWWW09 - Triplify Light-Weight Linked Data Publication from Relational Databases
WWW09 - Triplify Light-Weight Linked Data Publication from Relational Databases
 
Participatory Research
Participatory ResearchParticipatory Research
Participatory Research
 

Recently uploaded

"Testing of Helm Charts or There and Back Again", Yura Rochniak
"Testing of Helm Charts or There and Back Again", Yura Rochniak"Testing of Helm Charts or There and Back Again", Yura Rochniak
"Testing of Helm Charts or There and Back Again", Yura RochniakFwdays
 
"Platform Engineering with Development Containers", Igor Fesenko
"Platform Engineering with Development Containers", Igor Fesenko"Platform Engineering with Development Containers", Igor Fesenko
"Platform Engineering with Development Containers", Igor FesenkoFwdays
 
IT Nation Evolve event 2024 - Quarter 1
IT Nation Evolve event 2024  - Quarter 1IT Nation Evolve event 2024  - Quarter 1
IT Nation Evolve event 2024 - Quarter 1Inbay UK
 
LLMs, LMMs, their Improvement Suggestions and the Path towards AGI.pdf
LLMs, LMMs, their Improvement Suggestions and the Path towards AGI.pdfLLMs, LMMs, their Improvement Suggestions and the Path towards AGI.pdf
LLMs, LMMs, their Improvement Suggestions and the Path towards AGI.pdfThomas Poetter
 
LF Energy Webinar: Introduction to TROLIE
LF Energy Webinar: Introduction to TROLIELF Energy Webinar: Introduction to TROLIE
LF Energy Webinar: Introduction to TROLIEDanBrown980551
 
Introducing the New FME Community Webinar - Feb 21, 2024 (2).pdf
Introducing the New FME Community Webinar - Feb 21, 2024 (2).pdfIntroducing the New FME Community Webinar - Feb 21, 2024 (2).pdf
Introducing the New FME Community Webinar - Feb 21, 2024 (2).pdfSafe Software
 
Q1 Memory Fabric Forum: Building Fast and Secure Chips with CXL IP
Q1 Memory Fabric Forum: Building Fast and Secure Chips with CXL IPQ1 Memory Fabric Forum: Building Fast and Secure Chips with CXL IP
Q1 Memory Fabric Forum: Building Fast and Secure Chips with CXL IPMemory Fabric Forum
 
The Future of Product, by Founder & CEO, Product School
The Future of Product, by Founder & CEO, Product SchoolThe Future of Product, by Founder & CEO, Product School
The Future of Product, by Founder & CEO, Product SchoolProduct School
 
AI MODELS USAGE IN FINTECH PRODUCTS: PM APPROACH & BEST PRACTICES by Kasthuri...
AI MODELS USAGE IN FINTECH PRODUCTS: PM APPROACH & BEST PRACTICES by Kasthuri...AI MODELS USAGE IN FINTECH PRODUCTS: PM APPROACH & BEST PRACTICES by Kasthuri...
AI MODELS USAGE IN FINTECH PRODUCTS: PM APPROACH & BEST PRACTICES by Kasthuri...ISPMAIndia
 
Introduction to Multimodal LLMs with LLaVA
Introduction to Multimodal LLMs with LLaVAIntroduction to Multimodal LLMs with LLaVA
Introduction to Multimodal LLMs with LLaVARobert McDermott
 
Progress Report: Ministry of IT under Dr. Umar Saif Aug 23-Feb'24
Progress Report: Ministry of IT under Dr. Umar Saif Aug 23-Feb'24Progress Report: Ministry of IT under Dr. Umar Saif Aug 23-Feb'24
Progress Report: Ministry of IT under Dr. Umar Saif Aug 23-Feb'24Umar Saif
 
My self introduction to know others abut me
My self  introduction to know others abut meMy self  introduction to know others abut me
My self introduction to know others abut meManoj Prabakar B
 
"Running Open-Source LLM models on Kubernetes", Volodymyr Tsap
"Running Open-Source LLM models on Kubernetes",  Volodymyr Tsap"Running Open-Source LLM models on Kubernetes",  Volodymyr Tsap
"Running Open-Source LLM models on Kubernetes", Volodymyr TsapFwdays
 
Dynamical systems simulation in Python for science and engineering
Dynamical systems simulation in Python for science and engineeringDynamical systems simulation in Python for science and engineering
Dynamical systems simulation in Python for science and engineeringMassimo Talia
 
Early Tech Adoption: Foolish or Pragmatic? - 17th ISACA South Florida WOW Con...
Early Tech Adoption: Foolish or Pragmatic? - 17th ISACA South Florida WOW Con...Early Tech Adoption: Foolish or Pragmatic? - 17th ISACA South Florida WOW Con...
Early Tech Adoption: Foolish or Pragmatic? - 17th ISACA South Florida WOW Con...Adrian Sanabria
 
Breaking Barriers & Leveraging the Latest Developments in AI Technology
Breaking Barriers & Leveraging the Latest Developments in AI TechnologyBreaking Barriers & Leveraging the Latest Developments in AI Technology
Breaking Barriers & Leveraging the Latest Developments in AI TechnologySafe Software
 
21ST CENTURY LITERACY FROM TRADITIONAL TO MODERN
21ST CENTURY LITERACY FROM TRADITIONAL TO MODERN21ST CENTURY LITERACY FROM TRADITIONAL TO MODERN
21ST CENTURY LITERACY FROM TRADITIONAL TO MODERNRonnelBaroc
 
Unlocking the Cloud's True Potential: Why Multitenancy Is The Key?
Unlocking the Cloud's True Potential: Why Multitenancy Is The Key?Unlocking the Cloud's True Potential: Why Multitenancy Is The Key?
Unlocking the Cloud's True Potential: Why Multitenancy Is The Key?GleecusTechlabs1
 
Enhancing Productivity and Insight A Tour of JDK Tools Progress Beyond Java 17
Enhancing Productivity and Insight  A Tour of JDK Tools Progress Beyond Java 17Enhancing Productivity and Insight  A Tour of JDK Tools Progress Beyond Java 17
Enhancing Productivity and Insight A Tour of JDK Tools Progress Beyond Java 17Ana-Maria Mihalceanu
 

Recently uploaded (20)

"Testing of Helm Charts or There and Back Again", Yura Rochniak
"Testing of Helm Charts or There and Back Again", Yura Rochniak"Testing of Helm Charts or There and Back Again", Yura Rochniak
"Testing of Helm Charts or There and Back Again", Yura Rochniak
 
"Platform Engineering with Development Containers", Igor Fesenko
"Platform Engineering with Development Containers", Igor Fesenko"Platform Engineering with Development Containers", Igor Fesenko
"Platform Engineering with Development Containers", Igor Fesenko
 
IT Nation Evolve event 2024 - Quarter 1
IT Nation Evolve event 2024  - Quarter 1IT Nation Evolve event 2024  - Quarter 1
IT Nation Evolve event 2024 - Quarter 1
 
LLMs, LMMs, their Improvement Suggestions and the Path towards AGI.pdf
LLMs, LMMs, their Improvement Suggestions and the Path towards AGI.pdfLLMs, LMMs, their Improvement Suggestions and the Path towards AGI.pdf
LLMs, LMMs, their Improvement Suggestions and the Path towards AGI.pdf
 
LF Energy Webinar: Introduction to TROLIE
LF Energy Webinar: Introduction to TROLIELF Energy Webinar: Introduction to TROLIE
LF Energy Webinar: Introduction to TROLIE
 
Introducing the New FME Community Webinar - Feb 21, 2024 (2).pdf
Introducing the New FME Community Webinar - Feb 21, 2024 (2).pdfIntroducing the New FME Community Webinar - Feb 21, 2024 (2).pdf
Introducing the New FME Community Webinar - Feb 21, 2024 (2).pdf
 
Q1 Memory Fabric Forum: Building Fast and Secure Chips with CXL IP
Q1 Memory Fabric Forum: Building Fast and Secure Chips with CXL IPQ1 Memory Fabric Forum: Building Fast and Secure Chips with CXL IP
Q1 Memory Fabric Forum: Building Fast and Secure Chips with CXL IP
 
The Future of Product, by Founder & CEO, Product School
The Future of Product, by Founder & CEO, Product SchoolThe Future of Product, by Founder & CEO, Product School
The Future of Product, by Founder & CEO, Product School
 
AI MODELS USAGE IN FINTECH PRODUCTS: PM APPROACH & BEST PRACTICES by Kasthuri...
AI MODELS USAGE IN FINTECH PRODUCTS: PM APPROACH & BEST PRACTICES by Kasthuri...AI MODELS USAGE IN FINTECH PRODUCTS: PM APPROACH & BEST PRACTICES by Kasthuri...
AI MODELS USAGE IN FINTECH PRODUCTS: PM APPROACH & BEST PRACTICES by Kasthuri...
 
Introduction to Multimodal LLMs with LLaVA
Introduction to Multimodal LLMs with LLaVAIntroduction to Multimodal LLMs with LLaVA
Introduction to Multimodal LLMs with LLaVA
 
Progress Report: Ministry of IT under Dr. Umar Saif Aug 23-Feb'24
Progress Report: Ministry of IT under Dr. Umar Saif Aug 23-Feb'24Progress Report: Ministry of IT under Dr. Umar Saif Aug 23-Feb'24
Progress Report: Ministry of IT under Dr. Umar Saif Aug 23-Feb'24
 
My self introduction to know others abut me
My self  introduction to know others abut meMy self  introduction to know others abut me
My self introduction to know others abut me
 
"Running Open-Source LLM models on Kubernetes", Volodymyr Tsap
"Running Open-Source LLM models on Kubernetes",  Volodymyr Tsap"Running Open-Source LLM models on Kubernetes",  Volodymyr Tsap
"Running Open-Source LLM models on Kubernetes", Volodymyr Tsap
 
Dynamical systems simulation in Python for science and engineering
Dynamical systems simulation in Python for science and engineeringDynamical systems simulation in Python for science and engineering
Dynamical systems simulation in Python for science and engineering
 
Early Tech Adoption: Foolish or Pragmatic? - 17th ISACA South Florida WOW Con...
Early Tech Adoption: Foolish or Pragmatic? - 17th ISACA South Florida WOW Con...Early Tech Adoption: Foolish or Pragmatic? - 17th ISACA South Florida WOW Con...
Early Tech Adoption: Foolish or Pragmatic? - 17th ISACA South Florida WOW Con...
 
Breaking Barriers & Leveraging the Latest Developments in AI Technology
Breaking Barriers & Leveraging the Latest Developments in AI TechnologyBreaking Barriers & Leveraging the Latest Developments in AI Technology
Breaking Barriers & Leveraging the Latest Developments in AI Technology
 
21ST CENTURY LITERACY FROM TRADITIONAL TO MODERN
21ST CENTURY LITERACY FROM TRADITIONAL TO MODERN21ST CENTURY LITERACY FROM TRADITIONAL TO MODERN
21ST CENTURY LITERACY FROM TRADITIONAL TO MODERN
 
5 Tech Trend to Notice in ESG Landscape- 47Billion
5 Tech Trend to Notice in ESG Landscape- 47Billion5 Tech Trend to Notice in ESG Landscape- 47Billion
5 Tech Trend to Notice in ESG Landscape- 47Billion
 
Unlocking the Cloud's True Potential: Why Multitenancy Is The Key?
Unlocking the Cloud's True Potential: Why Multitenancy Is The Key?Unlocking the Cloud's True Potential: Why Multitenancy Is The Key?
Unlocking the Cloud's True Potential: Why Multitenancy Is The Key?
 
Enhancing Productivity and Insight A Tour of JDK Tools Progress Beyond Java 17
Enhancing Productivity and Insight  A Tour of JDK Tools Progress Beyond Java 17Enhancing Productivity and Insight  A Tour of JDK Tools Progress Beyond Java 17
Enhancing Productivity and Insight A Tour of JDK Tools Progress Beyond Java 17
 

Enterprise knowledge graphs

  • 1. Enterprise Knowledge Graphs Sören Auer https://www.eccenca.com
  • 2. The three Big Data „V“ – Variety is often neglected Quelle: Gesellschaft für Informatik Sören Auer 2
  • 3. Linked Data Principles Addressing the neglected third V (Variety) 1. Use URIs to identify the “things” in your data 2. Use http:// URIs so people (and machines) can look them up on the web 3. When a URI is looked up, return a description of the thing (in RDF format) 4. Include links to related things http://www.w3.org/DesignIssues/LinkedData.html 3 [1] Auer, Lehmann, Ngomo, Zaveri: Introduction to Linked Data and Its Lifecycle on the Web. Reasoning Web 2013
  • 4. Linked (Open) Data: The RDF Data Model 4 RDF = Resource Description Framework located in label industry headquarters full nameDHL Post Tower 162.5 m Bonn Logistics Logistik DHL International GmbH height 物流 label Sören Auer
  • 5. RDF Data Model (a bit more technical) – Graph consists of: • Resources (identified via URIs) • Literals: data values with data type (URI) or language (multilinguality integrated) • Attributes of resources are also URI-identified (from vocabularies) – Various data sources and vocabularies can be arbitrarily mixed and meshed – URIs can be shortened with namespace prefixes; e.g. dbp: → http://dbpedia.org/resource/ gn:locatedIn rdfs:label dbo:industry ex:headquarters foaf:namedbp:DHL_International_GmbH dbp:Post_Tower "162.5"^^xsd:decimal dbp:Bonn dbp:Logistics "Logistik"@de "DHL International GmbH"^^xsd:string ex:height "物流"@zh rdfs:label rdf:value unit:Meter ex:unit
  • 6. RDF mediates between different Data Models & bridges between Conceptual and Operational Layers Id Title Screen 5624 SmartTV 104cm 5627 Tablet 21cm Prod:5624 rdf:type Electronics Prod:5624 rdfs:label “SmartTV” Prod:5624 hasScreenSize “104”^^unit:cm ... Electronics Vehicle Car Bus Truck Vehicle rdf:type owl:Thing Car rdfs:subClassOf Vehicle Bus rdfs:subClassOf Vehicle ... Tabular/Relational Data Taxonomic/Tree Data Logical Axioms / Schema Male rdfs:subClassOf Human Female rdfs:subClassOf Human Male owl:disjointWith Female ... Sören Auer 6
  • 7. © Fraunhofer · Seite 7 Vocabulary Example Vocabulary Schema Instantiation PostTower rdf:type Building PostTower locatedIn dbpedia:Bonn PostTower height "162.5"^^meter located in label industry headquarters full nameDHL Post Tower 162.5 m Bonn Logistics Logistik DHL International GmbH height 物流 label Class: Company Property Expected type inIndustry Industry fullName String headquarter Building Class: Building Property Expected type locatedIn Industry height unit:meter RDFRepresentationVisualRepresentation Company rdf:type rdfs:Class Building rdf:type rdfs:Class inIndustry rdf:type rdfs:Property inIndustry rdfs:domain Company inIndustry rdfs:range Industry headquarter rdf:type rdfs:Property headquarter rdfs:domain Company headquarter rdfs:range Building DHL rdf:type Company DHL fullName "DHL Int. GmbH" DHL inIndustry Logistics DHL headquarter PostTower
  • 8. © Fraunhofer · Seite 8 Semantic Web Layer Cake 2001 http://www.w3.org/2001/10/03-sww-1/slide7-0.html • Monolithic based on XML • Focus on heavyweight Semantic (Ontologies, Logic, Reasoning)
  • 9. © Fraunhofer The Semantic Web Layer Cake 2015 – Bridging between Big & Smart Data Unicode URIs XML JSON CSV RDB HTML RDF RDF/XML JSON-LD CSV2RDF R2RML RDFa RDF Data Shapes RDF-Schema Vocabularies OntologienSKOS Thesauri LogikSWRL Regeln SPARQL (Accesscontrol),Signatur, Encryption(HTTPS/CERT/DANE), • Lingua Franca of Data integration with many technology interfaces (XML, HTML, JSON, CSV, RDB,…) • Focus on lightweight vocabularies, rules, thesauri etc. • Less “invasive”
  • 10. © Fraunhofer RDF - the Lingua Franca of Data Integration • RDF is simple • We can easily encode and combine all kinds of data models (relational, taxonomic, graphs, object-oriented, …) • RDF supports distributed data and schema • We can seamlessly evolve simple semantic representations (vocabularies) to more complex ones (e.g. ontologies) • Small representational units (URI/IRIs, triples) facilitate mixing and mashing • RDF can be viewed from many perspectives: facts, graphs, ER, logical axioms, graphs, objects • RDF integrates well with other formalisms - HTML (RDFa), XML (RDF/XML), JSON (JSON-LD), CSV, … • Linking and referencing between different knowledge bases, systems and platforms facilitates the creation of sustainable data ecosystems 10
  • 11. © Fraunhofer Successful application domains Linked Data & Semantic Integration Search Engine Optimization & Web-Commerce  Schema.org used by >20% of Web sites  Major search engines exploit semantic desciptions Pharma, Lifesciences  Mature, comprehensive vocabularies and ontologies  Billions of disease, drug, clinical trial descriptions Digital Libraries  Many established vocabularies (DublinCore, FRBR, EDM)  Millions of aggregated from thousends of memory institutions in Europeana, German Digital Library
  • 12. © Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS The Web evolves into a Web of Data Sören Auer 12 Linked Open Data Facebook Open Graph
  • 13. © Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS Knowledge Graphs – A definition • Fabric of concept, class, property, relationships, entity descriptions • Uses a knowledge representation formalism (typically RDF, RDF-Schema, OWL) • Holistic knowledge (multi-domain, source, granularity): • instance data (ground truth), • open (e.g. DBpedia, WikiData), private (e.g. supply chain data), closed data (product models), • derived, aggregated data, • schema data (vocabularies, ontologies) • meta-data (e.g. provenance, versioning, documentation licensing) • comprehensive taxonomies to categorize entities • links between internal and external data • mappings to data stored in other systems and databases
  • 14. © Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS Knowledge Graph Challenges & Opportunities Knowledge graphs typically cover • Multiple domains • Various levels of granularity • Data from multiple sources • Various degrees of structure Challenges • Quality • Coherence • Co-evolution • Update propagation • Curation & interaction Opportunities • Background knowledge for various applications (e.g. question answering, data integration, machine learning) • Facilitate intra-organizational data sharing and exchange (data value chains) 14
  • 15. © Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS Comparison of various enterprise data integration paradigms Paradigm Data Model Integr. Strategy Conceptual/ operational Hetero- geneous data Intern./ extern. data No. of sources Type of integr. Domain coverage Se- mantic repres. XML Schema DOM trees LaV operational   medium both medium high Data Warehouse relational GaV operational - partially medium physical small medium Data Lake various LaV operational   large physical high medium MDM UML GaV conceptual - - small physical small medium PIM / PCS trees GaV operational partially partially - physical medium medium Enterprise search document - operational  partially large virtual high low EKG RDF LaV both   medium both high very high [1] Michael Galkin, Sören Auer, Simon Screrri: Enterprise Knowledge Graphs: A Survey. Submitted to 37th International Conference on Information Systems. 2016.
  • 16. © Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS Knowledge Graph Technology 16
  • 17. Adding a Semantic Layer to Data Lakes 17 Management Accounting Marketing Sales SupportR&D Semantic Data Lake • central place for model, schema and data historization • Combination of Scale Out (cost reduction) and semantics (increased control & flexibility) • grows incrementally (pay-as-you-go) Inbound Data Sources Outbound and Consumption Inbound Raw Data Store Data Lake (order of magnitude cheaper scalable data store) Knowledge Graph for Relationship Definition and Meta Data Frontend to Access Relationship and KPI Definition / Documentation Frontend to Access (ad hoc) Reports Outbound Data Delivery to Target Systems JSON-LD CSVW R2RMLXML2RDF © eccenca.com See also https://www.eccenca.com/en/products-corporate-memory.html
  • 18. W3C R2RML – Relational to RDF Mapping Sören Auer 18 R2RML: RDB to RDF Mapping Language, W3C Recommendation 27 September 2012 Editors: Souripriya Das, Seema Sundara, Richard Cyganiak http://www.w3.org/TR/r2rml/
  • 20. 1. Either resulting RDF knowledge base is materialized in a triple store & 2. subsequently queried using SPARQL 3. or the materialization step is avoided by dynamically mapping an input SPAQRL query into a corresponding SQL query, which renders exactly the same results as the SPARQL query being executed against the materialized RDF dump SPARQLMap – Mapping RDB 2 RDF
  • 21. Example: Sparqlify • Rationale: Exploit existing formalisms (SQL, SPARQL Construct) as much as possible • flexible & versatile mapping language • translating one SPARQL query into exactly one efficiently executable SQL query • Solid theoretical formalization based on SPARQL-relational algebra transformations • Extremely scalable through elaborated view candidate selection mechanism • Used to publish 20B triples for LinkedGeoData [1] Stadler, Unbehauen, Auer, Lehmann: Sparqlify – Very Large Scale Linked Data Publication from Relational Databases. [2] Unbehauen, Stadler, Auer: Optimizing SPARQL-to-SQL Rewriting. iiWAS 2013 [3] Auer, et al.: Triplify: light-weight linked data publication from relational databases. WWW 2009 SPARQL Construct SQL View Bridge
  • 22. Semantified Big Data Architecture Blueprint Sören Auer 22 [1] Mami, Scerri, Auer, Vidal: Towards the Semantification of Big Data Technology. DEXA 2016 Datasources Ingestion Storage Semantic Lifting with Mappings Querys Storing of semantic and semantified data in Apache Parquet files on HDFS
  • 24. SEBIDA Evaluation Results • Loads data faster • Has quite different query performance characteristics – faster in 5 out of 12 queries, similar performance in 2, slower in 5 Sören Auer 24
  • 25. © Fraunhofer · Seite 25 VOCOL: COLLABORATIVE VOCABULARY CURATION ENVIRONMENT Comprehensive Support for Evolving Vocabularies
  • 26. © Fraunhofer · Seite 26 Industry 4.0 Semantic Models as Bridge between Shop & Office Floor
  • 27. © Fraunhofer · Seite 27 Semantic Administrative Shell & Reference Architecture for Industry 4.0 (RAMI4.0) Administrative Shell (Verwaltungsschale) provides a digital identity for arbitrary Industry 4.0 components (e.g. sensors, actors/robots) exposing data covering the whole life-cycle Reference Architecture for Industry 4.0 (RAMI4.0) provides a conceptual framework for implementing comprehensive Industry 4.0 scenarios We have implemented both concepts along with a number of IEC and ISO standards in a comprehensive information model ready to be implemented in productive environments
  • 28. © Fraunhofer · Seite 28 VoCol collaborative Development Environment for Vocabularies Versioning Git/Bitbucket Issue tracking GitLab/ GitHub Syntax validation Docu- mentation generation Authoring Turtle Visualization vOWL Publishing LOD/Sparql Integrates a number of tools & services for different aspects of vocabulary development Is centered around Git version control (or Bitbucket), thus supporting the branching and merging of vocabularies Supports the roundtrip between • Schema/vocabulary development • Competency questions (expressed in SPARQL) • Example data  Bridges between conceptual models and executable code http://eis.iai.uni-bonn.de/Projects/VoCol.html
  • 29. © Fraunhofer · Seite 29 Development based on Git – Version Control Git is meanwhile the most widely used version control system. It is a distributed revision control system with an emphasis on speed, data integrity, and support for distributed, non-linear workflows. Git was initially designed and developed in 2005 by Linux kernel developers for Linux kernel development Git is the basis for a variety of open-source or commercial services and products such as: GitHub/Bitbucket - Web-based Git repository hosting service with millions of users GitLab/Gitolite - open-source Web-based Git repository management platforms Since TeamFoundationServer release 2013, Microsoft added native support for Git Git is easily extensible and integratable into arbitrary workflows via GitHooks
  • 32. © Fraunhofer · Seite 32 Environment: Dynamic Documentation
  • 33. © Fraunhofer · Seite 33 VoCol Environment: Dynamic Visualization
  • 34. © Fraunhofer · Seite 34 VoCol Environment: Analytics
  • 36. © Fraunhofer · Seite 36 VoCol Environment: Integrated SPARQL Querying, e.g. for checking competency questions
  • 40. © Fraunhofer · Seite 40 INDUSTRIAL DATA SPACE
  • 41. © Fraunhofer · Seite 41 Vocabulary-based Integration facilitates Data-driven Businesses Vocablary
  • 42. © Fraunhofer ·· Seite 42 Die Arbeiten zum Industrial Data Space sind komplementär verzahnt mit der Plattform Industrie 4.0 Handel 4.0 Bank 4.0Versicherung 4.0 …Industrie 4.0 Fokus auf die produzierende Industrie Smart Services Übertragung, Netzwerke Echtzeitsysteme Industrial Data Space Fokus auf Daten Daten …
  • 43. © Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS The Industrial Data Space Initiative Community of >30 large German and European Companies Pre-competitive, publicly funded innovation project involving 11 Fraunhofer institutes for developing IDS reference architecture Current members of the Industrial Data Space Association
  • 44. © Fraunhofer · Seite 44 Bilder: ©Fotolia Francesco De Paoli, Nmedia, hakandogu Semantic Data Linking for Enterprise Data Value Chains Data Lake Pure Internet centralized, monopolistic federated, secure, „trusted“, standard-based completely dezentral, open, unsecure Data management Central Repository Decentral Decentral Data Ownership Central Decentral Decentral Data Linking Single provider Federated, on demand Missing Data Security Bilateral Certified system Bilateral Market structure Central Provider Role system Unstructured Transport infrastructure Internet Internet Internet Industrial Data Space
  • 45. © Fraunhofer · Seite 45 Bilder: © Fotolia 77260795 ∙ 73040142 58947296 ∙ 68898041 Basic principles of the Industrial Data Space On Demand Vernetzung Linked Light Semantics Security with Industrial Data Container Certified Roles On Demand Interlinking
  • 46. © Fraunhofer · Seite 46 Bildquellen: Istockphoto Industrial Data Space: On Demand Interlinking Service A Service C Service E Service B Service D Service G Service F Enterprise 4 Enterprise 1 Enterprise 6 Enterprise 2 Enterprise 3 Enterprise 5 All Data stays with its Ownern and are controlled and secured. Only on request for a service data will be shared. No central platform.
  • 47. © Fraunhofer · Seite 47 --- VERTRAULICH --- Industrial Data Space Upload / Download / Search Internet AppsVocabulary Industrial Data Space Broker Clearing RegistryIndex Industrial Data Space App Store Internal IDS Connector Company A Internal IDS Connector Company B External IDS Connector External IDS Connector Upload Third Party Cloud Provider Download Upload / Download © Fraunhofer IDS Architecture Overview
  • 48. Big Data is not Just Volume and Velocity Variety (& Varacity) are key challenges Linked Data helps dealing with both • Linked Data life-cycle requires to integrate and adapt results from a number of disciplines – NLP, – Machine Learning, – Knowledge Representation, – Data Management, – User Interaction – … • Applications in a number of domains – cultural heritage, – life sciences, – industry 4.0 / cyber-physical systems, – smart cities, – mobility, – … Sören Auer 48 Linked Data links not only data but also: • Various disciplines • Applications and Use cases
  • 49. Creating Knowledge out of Interlinked Data Thanks for your attention! Sören Auer http://www.iai.uni-bonn.de/~auer | http://eis.iai.uni-bonn.de auer@cs.uni-bonn.de https://www.eccenca.com

Editor's Notes

  1. http://www.gi.de/nc/service/informatiklexikon/detailansicht/article/big-data.html
  2. Data Lake is a storage repository for big data scale raw data in original data formats. late binding approach to schema: “Let us decide, when we need it.” scale out architecture on commodity infrastructure, mostly with HFS/Hadoop/Spark, which gives a huge cost advantage – about factor 10 compared to data warehouses. Semantic Data Lake = Data Lake + Knowledge Graph management of structure (vocabularies/schemas, KPIs trees, metadata, …) on top of the Data Lake is performed in a knowledge graph - a complex data fabric representing all kinds of things and how they relate to each other. A knowledge graph is unique regarding flexibility, multiple views and metadata capabilities. Based on the Resource Description Framework (RDF) standard and Linked Data principles.
  3. Die Plattform bietet einen sicheren Raum zur Vernetzung Daten bleiben bei den Enterprise und werden nur bei Bedarf vernetzt Marktorientiertes Modell ohne Abhängigkeiten von einzelnen Anbietern Wertschöpfung und Servicee bleiben beim Enterprise Finanzierung über Servicee, nicht über Werbung oder Datenverkauf Keine zentrale Datenkrake wie Google, sondern Kontrolle über Daten bleibt bei den Daten-Ownern Kunde (Endnutzer) ist nicht Produkt, sondern Souverän über seine Daten Das Ganze ist mehr als die Summe der einzelnen Teile (Ende-zu-Ende-Servicee auf Basis der Daten von mehreren bieten überproportional höheren Mehrwert) Kein zentraler Datentopf, sondern ein Netz gesunder, sicherer Daten Governance nicht monopolistisch, sondern föderal
  4. Linked Data approach can help to establish data value chains Linked Data life-cycle requires to integrate and adapt results from a number of disciplines (NLP, Machine Learning, Knowledge Representation, Data Management) Applications in a number of domains (cultural heritage, life sciences, industry 4.0 / cyber-physical systems, smart cities, mobility,…)