Cognitive data

Sören Auer
Sören AuerProfessor for Data Science & Digital Libraries
Stockholm, November 11, 2018
KTH Royal Institute of Technology
From Linked Data to
Cognitive Data
--- VERTRAULICH ---
Zuse Z3: the
beginning of
Computing –
close to the
hardware
Foto: Konrad Zuse
Internet
Archiv/Deutsches
Museum/DFG
© Fraunhofer
--- VERTRAULICH ---
We can make things
more intuitive
Picture: The illustrated recipes
of lucy eldridge
http://thefoxisblack.com/2013/
07/18/the-illustrated-recipes-
of-lucy-eldridge/
Computing more inuitive: procedural programming
Sören Auer 6
Computing more inuitive: OO programming
Sören Auer 8
Sören Auer 9
Computing even more inuitive: with cognitive data?!
Page 10
Machine Learning and Big Data
http://www.spacemachine.net/views/2016/3/datasets-over-algorithms
 AI is not just the next hype after Big Data, Big Data is the
reason why we have AI!
Page 11
Source: Gesellschaft für
Informatik
The Three “V” of Big Data - Variety often Neglected
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 the W3C
Resource Description Format (RDF)
4. Include links to related things
http://www.w3.org/DesignIssues/LinkedData.html
12
[1] Auer, Lehmann, Ngomo, Zaveri: Introduction to Linked Data and Its Lifecycle on the Web. Reasoning Web 2013
Page 13
1. Graph based RDF data model consisting of S-P-O statements (facts)
RDF & Linked Data in a Nutshell
OSLCFest
dbpedia:Stockholm
05.11.2018
KTH
conf:organizes
conf:starts
conf:takesPlaceIn
2. Serialised as RDF Triples:
KTH conf:organizes OSLCFest .
OSLCFest conf:starts “2018-11-05”^^xsd:date .
OSLCFest conf:takesPlaceAt dbpedia:Stockholm .
3. Publication under URL in Web, Intranet, Extranet
Subject Predicate Object
Page 14
Creating Knowledge Graphs with RDF
Linked Data
located in
label
industry
headquarters
full nameDHL
Post Tower
162.5 m
Bonn
Logistics Logistik
DHL International GmbH
height
物流
label
Page 15
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/
RDF Data Model (a bit more technical)
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
Vocabularies – Breaking the mold!
• Semantic data virtualization allows for continuous expansion and enhancement of data and
metadata across data sources without loosing the overall perspective
Relational
data models
1:1 Relation between
Data Model und Application
Graph based
data model
Subject
Predicate
Object / Subject
Predicate
Object / Subject
1:n Relation between
Data Model and Application
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 17
18
Engineering Manufactur. Logistics Marketing. . .
Parts of data are being curated, duplicated, annotated and simply
changed over time, making reconciliation and interpretation a challenge
Perspectives on data turn into silos
Engineering Manufactur. Logistics Marketing
19
Integrate Using RDF & Vocabularies
Page 20
The Trinity of Semantic Integration
Knowledge Graphs
• Complex fabric of concepts
& relationships
• Focus on heterogenous,
multi-domain knowledge
representation
Data Spaces
• Community of
organizations agreeing on
standards for data access/
security/ semantics/
governance/ licenses
• Focus on data sharing &
exchange
Semantic Data Lakes
• Storage facility for
enterprise/research data
• Use Big Data (HDFS)
management
• Focus on scalable data
access
Use in a single organization Intra-organizational use
Page 21
• 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
Knowledge Graphs – A definition
Smart Data for
Machine Learning
Page 22
Page 23
Search Engine Optimization & Web-Commerce
 Schema.org used by >20% of Web sites
 Major search engines exploit semantic descriptions
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 thousands of memory
institutions in Europeana, German Digital Library
Emerging Knowledge Graphs & Data Spaces
ENTERPRISE DATA INTEGRATION WITH A
SEMANTIC DATA LAKE
Example:
© eccenca GmbH 2016
The future of data management is semantic!
The Problem today Solution Tomorrow
App. 1 App. 2 App. 3 App. 1 App. 2 App. 3
Data Access limited
to connected source
Exploding cost
of ETL
Full Access to All Data
Lean Architecture
Great Synergies in data
lifting
Management
Accounting
Risk Management
Regulatory Reporting
Treasury MarketingAccounting
Corporate
Memory
Inbound
Data Sources
Outbound and
Consumption
Inbound Raw Data Store
Knowledge Graph for Meta Data, KPI Definition and Data Models
Frontend to Access Relationship and KPI Definition /
Documentation
Frontend to Access (ad hoc) Reports
Outbound Data Delivery to Target
Systems
Big Data DWH-
Infrastructure
High Level Architecture
Corporate Memory
Integration via Knowledge Graph and
Semantic Data Models
27
Knowledge Graph
(RDF)
XML
EDI
CSV
iDoc
RDF
JSON
XML
EDI
CSV
iDoc
RDF
JSON
Supplier OnBoarding cost/time reduction due to rich and flexible pivot format
OEMSupplier
© eccenca GmbH 2016
lift
ERP
sync
OEM
CMEM
Supplier
CMEM
ERP
CMEM-SYNC
tabulate
subscribe
Turning Strings into Things for Graph
Synchronization
CMEM-SYNC
Ingestion / Cataloging
• Cataloging of datasets and
vocabularies
• Rich meta data model
• Automatic profiling of datasets
• DataLake (HDFS) integration
• Extraction of metadata
• Continuous monitoring for new
versions and structural changes
29
Ingestion
Cataloging
Mapping Discovery Linking Selection Analytics, Experiments
Manage Datasets
30
Profiling Data
31
Mapping
• Sophisticated mapping management
• Mapping towards semantic vocabularies
(lifting)
• Self documentation of data (data
dictionary)
• Normalization of data
• Mapping suggestions
• Mapping reuse based on data profiling
• Advanced mapping suggestions
• machine learning
• data fingerprinting
32
Ingestion
Cataloging
Mapping Discovery Linking Selection Analytics, Experiments
Discovery
• Calculation of dataset
relatedness / similarity
• Visual exploration of
data neighborhood
• Similarity measure based
on profiling and mapping
• Similarity measure based
on data fingerprinting
33
Ingestion
Cataloging
Mapping Discovery Linking Selection Analytics, Experiments
Linking
• Linking based on expressive rule
trees
• Interactive machine learning of
linkage rules
• Continuous integration of gold
standard for quality assurance
• Data fusion support
34
Ingestion
Cataloging
Mapping Discovery Linking Selection Analytics, Experiments
© eccenca GmbH 2016
Create Declarative Matching Rules
Create Context-aware deterministic rules to match pairs of records, supported by machine learning.
© eccenca GmbH 2016
© Fraunhofer
Industrial/International
Data Space
Establishing Data Value Chains
© Fraunhofer 37
Digitisation of Industry
Digitisation Enables Data Driven Business Models
… for Example Precision Farming
Image sources: wiwo, traction-magazin.de. Quelle: Beecham Research Ltd. (2014).
“Precision Farming” Value Creation in the “Ecosystem”
“Digital
Farming
Eco-
system”
Machine
Producer
Seed
Provider
Farmers
Wholesale
Technology
Provider
Weather
Service
© Fraunhofer 38
Goal and Architecture of the Industrial Data Space
Der Industrial Data Space aims at blueprinting a
“Network of Trusted Data”.
Secure
Data
exchange
Trustworthiness
Certified
Members
Decentralisation
Federated
Architecture Sovereignty
over Data
and Services
Governance
Common Rules
of the Game
Scalability
Network Effects
Openness
Neutral and
User-Driven Ecosystem
Platform and
Services
© Fraunhofer 39
Goal and Architecture of the Industrial Data Space
Component Reference Architecture
© Fraunhofer
www.industrialdataspace.or
g
// 40
LOCATION IN THE CONTEXT OF “INDUSTRY 4.0”
FOCUS ON DATA
Retail 4.0 Bank 4.0Insurance
4.0
…
Industrie 4.0
Focus on Manufacturing
Industry
Smart Services
Transfer and
Networks
Real time systems
Industrial Data Space
Focus on Data
Data
…
© Fraunhofer 41
Goal and Architecture of the Industrial Data Space
The Industrial Data Space Connects the Internet of
Things and Smart Services.
Integration Millions of Metadata
Records from >2000 Memory
Institutions for the German Digital
Library
A Cultural Heritage Data Space
--- VERTRAULICH ---
43
Dataspace with
• 2000 memory institutions in Germany alone
• Common semantic data model: EDM
• Common data governance: CC0
• Common access scheme: OAI-PMH
--- VERTRAULICH ---
--- VERTRAULICH ---
Conclusion
Page 47
Hybrid AI – combination of smart data (knowledge graphs) and smart analytics
Distributed semantic technologies – knowledge representation using vocabularies,
ontologies
Question Answering
• Open Question Answering architecture – flexible, knowledge-based integration
architecture for QA components and pipelines
• Dialogue Systems - combination of language models and goal-driven question
answering
Integration with Crowdsourcing
Knowlege Graphs, Semantic Data Lakes
Robotics – usage of semantics for actuation
Agile Interoperability – leveraging community driven vocabulary development
Cognitive Data challenges where we can
make a difference
 Systematic Enterprise
Linked Data Framework
(GDPR is a driver)
https://de.linkedin.com/in/soerenauer
https://twitter.com/soerenauer
https://www.xing.com/profile/Soeren_Auer
http://www.researchgate.net/profile/Soeren_Auer
TIB & Leibniz University of Hannover
auer@tib.eu
Prof. Dr. Sören Auer
1 of 48

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Cognitive data

  • 1. Stockholm, November 11, 2018 KTH Royal Institute of Technology From Linked Data to Cognitive Data
  • 2. --- VERTRAULICH --- Zuse Z3: the beginning of Computing – close to the hardware Foto: Konrad Zuse Internet Archiv/Deutsches Museum/DFG
  • 4. --- VERTRAULICH --- We can make things more intuitive Picture: The illustrated recipes of lucy eldridge http://thefoxisblack.com/2013/ 07/18/the-illustrated-recipes- of-lucy-eldridge/
  • 5. Computing more inuitive: procedural programming
  • 7. Computing more inuitive: OO programming
  • 9. Sören Auer 9 Computing even more inuitive: with cognitive data?!
  • 10. Page 10 Machine Learning and Big Data http://www.spacemachine.net/views/2016/3/datasets-over-algorithms  AI is not just the next hype after Big Data, Big Data is the reason why we have AI!
  • 11. Page 11 Source: Gesellschaft für Informatik The Three “V” of Big Data - Variety often Neglected
  • 12. 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 the W3C Resource Description Format (RDF) 4. Include links to related things http://www.w3.org/DesignIssues/LinkedData.html 12 [1] Auer, Lehmann, Ngomo, Zaveri: Introduction to Linked Data and Its Lifecycle on the Web. Reasoning Web 2013
  • 13. Page 13 1. Graph based RDF data model consisting of S-P-O statements (facts) RDF & Linked Data in a Nutshell OSLCFest dbpedia:Stockholm 05.11.2018 KTH conf:organizes conf:starts conf:takesPlaceIn 2. Serialised as RDF Triples: KTH conf:organizes OSLCFest . OSLCFest conf:starts “2018-11-05”^^xsd:date . OSLCFest conf:takesPlaceAt dbpedia:Stockholm . 3. Publication under URL in Web, Intranet, Extranet Subject Predicate Object
  • 14. Page 14 Creating Knowledge Graphs with RDF Linked Data located in label industry headquarters full nameDHL Post Tower 162.5 m Bonn Logistics Logistik DHL International GmbH height 物流 label
  • 15. Page 15 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/ RDF Data Model (a bit more technical) 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
  • 16. Vocabularies – Breaking the mold! • Semantic data virtualization allows for continuous expansion and enhancement of data and metadata across data sources without loosing the overall perspective Relational data models 1:1 Relation between Data Model und Application Graph based data model Subject Predicate Object / Subject Predicate Object / Subject 1:n Relation between Data Model and Application
  • 17. 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 17
  • 18. 18 Engineering Manufactur. Logistics Marketing. . . Parts of data are being curated, duplicated, annotated and simply changed over time, making reconciliation and interpretation a challenge Perspectives on data turn into silos
  • 19. Engineering Manufactur. Logistics Marketing 19 Integrate Using RDF & Vocabularies
  • 20. Page 20 The Trinity of Semantic Integration Knowledge Graphs • Complex fabric of concepts & relationships • Focus on heterogenous, multi-domain knowledge representation Data Spaces • Community of organizations agreeing on standards for data access/ security/ semantics/ governance/ licenses • Focus on data sharing & exchange Semantic Data Lakes • Storage facility for enterprise/research data • Use Big Data (HDFS) management • Focus on scalable data access Use in a single organization Intra-organizational use
  • 21. Page 21 • 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 Knowledge Graphs – A definition Smart Data for Machine Learning
  • 23. Page 23 Search Engine Optimization & Web-Commerce  Schema.org used by >20% of Web sites  Major search engines exploit semantic descriptions 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 thousands of memory institutions in Europeana, German Digital Library Emerging Knowledge Graphs & Data Spaces
  • 24. ENTERPRISE DATA INTEGRATION WITH A SEMANTIC DATA LAKE Example:
  • 25. © eccenca GmbH 2016 The future of data management is semantic! The Problem today Solution Tomorrow App. 1 App. 2 App. 3 App. 1 App. 2 App. 3 Data Access limited to connected source Exploding cost of ETL Full Access to All Data Lean Architecture Great Synergies in data lifting
  • 26. Management Accounting Risk Management Regulatory Reporting Treasury MarketingAccounting Corporate Memory Inbound Data Sources Outbound and Consumption Inbound Raw Data Store Knowledge Graph for Meta Data, KPI Definition and Data Models Frontend to Access Relationship and KPI Definition / Documentation Frontend to Access (ad hoc) Reports Outbound Data Delivery to Target Systems Big Data DWH- Infrastructure High Level Architecture Corporate Memory
  • 27. Integration via Knowledge Graph and Semantic Data Models 27 Knowledge Graph (RDF) XML EDI CSV iDoc RDF JSON XML EDI CSV iDoc RDF JSON Supplier OnBoarding cost/time reduction due to rich and flexible pivot format OEMSupplier
  • 28. © eccenca GmbH 2016 lift ERP sync OEM CMEM Supplier CMEM ERP CMEM-SYNC tabulate subscribe Turning Strings into Things for Graph Synchronization CMEM-SYNC
  • 29. Ingestion / Cataloging • Cataloging of datasets and vocabularies • Rich meta data model • Automatic profiling of datasets • DataLake (HDFS) integration • Extraction of metadata • Continuous monitoring for new versions and structural changes 29 Ingestion Cataloging Mapping Discovery Linking Selection Analytics, Experiments
  • 32. Mapping • Sophisticated mapping management • Mapping towards semantic vocabularies (lifting) • Self documentation of data (data dictionary) • Normalization of data • Mapping suggestions • Mapping reuse based on data profiling • Advanced mapping suggestions • machine learning • data fingerprinting 32 Ingestion Cataloging Mapping Discovery Linking Selection Analytics, Experiments
  • 33. Discovery • Calculation of dataset relatedness / similarity • Visual exploration of data neighborhood • Similarity measure based on profiling and mapping • Similarity measure based on data fingerprinting 33 Ingestion Cataloging Mapping Discovery Linking Selection Analytics, Experiments
  • 34. Linking • Linking based on expressive rule trees • Interactive machine learning of linkage rules • Continuous integration of gold standard for quality assurance • Data fusion support 34 Ingestion Cataloging Mapping Discovery Linking Selection Analytics, Experiments
  • 35. © eccenca GmbH 2016 Create Declarative Matching Rules Create Context-aware deterministic rules to match pairs of records, supported by machine learning. © eccenca GmbH 2016
  • 37. © Fraunhofer 37 Digitisation of Industry Digitisation Enables Data Driven Business Models … for Example Precision Farming Image sources: wiwo, traction-magazin.de. Quelle: Beecham Research Ltd. (2014). “Precision Farming” Value Creation in the “Ecosystem” “Digital Farming Eco- system” Machine Producer Seed Provider Farmers Wholesale Technology Provider Weather Service
  • 38. © Fraunhofer 38 Goal and Architecture of the Industrial Data Space Der Industrial Data Space aims at blueprinting a “Network of Trusted Data”. Secure Data exchange Trustworthiness Certified Members Decentralisation Federated Architecture Sovereignty over Data and Services Governance Common Rules of the Game Scalability Network Effects Openness Neutral and User-Driven Ecosystem Platform and Services
  • 39. © Fraunhofer 39 Goal and Architecture of the Industrial Data Space Component Reference Architecture
  • 40. © Fraunhofer www.industrialdataspace.or g // 40 LOCATION IN THE CONTEXT OF “INDUSTRY 4.0” FOCUS ON DATA Retail 4.0 Bank 4.0Insurance 4.0 … Industrie 4.0 Focus on Manufacturing Industry Smart Services Transfer and Networks Real time systems Industrial Data Space Focus on Data Data …
  • 41. © Fraunhofer 41 Goal and Architecture of the Industrial Data Space The Industrial Data Space Connects the Internet of Things and Smart Services.
  • 42. Integration Millions of Metadata Records from >2000 Memory Institutions for the German Digital Library A Cultural Heritage Data Space
  • 43. --- VERTRAULICH --- 43 Dataspace with • 2000 memory institutions in Germany alone • Common semantic data model: EDM • Common data governance: CC0 • Common access scheme: OAI-PMH
  • 47. Page 47 Hybrid AI – combination of smart data (knowledge graphs) and smart analytics Distributed semantic technologies – knowledge representation using vocabularies, ontologies Question Answering • Open Question Answering architecture – flexible, knowledge-based integration architecture for QA components and pipelines • Dialogue Systems - combination of language models and goal-driven question answering Integration with Crowdsourcing Knowlege Graphs, Semantic Data Lakes Robotics – usage of semantics for actuation Agile Interoperability – leveraging community driven vocabulary development Cognitive Data challenges where we can make a difference  Systematic Enterprise Linked Data Framework (GDPR is a driver)

Editor's Notes

  1. Die Z3 war der erste funktionsfähige Digitalrechner weltweit und wurde 1941 von Konrad Zuse in Zusammenarbeit mit Helmut Schreyer in Berlin gebaut. Die Z3 wurde in elektromagnetischer Relaistechnik mit 600 Relais für das Rechenwerk und 1400 Relais für das Speicherwerk ausgeführt.
  2. Longquan stoneware incense burner, China, 12th-13th century AD. Part of the Percival David Collection of Chinese Ceramics.
  3. Breakthroughs in AI come after data is available, not after algorithmic discoveries If you think about AI, think about the data, not algorithms Fun fact: most major AI companies share their internal deep learning toolkits
  4. Map the silos to their domain appropriate schemas Link the nodes (Linked Data) The schema can be virtual – multiple schemas/views may be appropriate
  5. Map the silos to their domain appropriate schemas Link the nodes (Linked Data) The schema can be virtual – multiple schemas/views may be appropriate
  6. You could argue: That MDM & BI Hub-Spoke systems have had the objective of the “Solution Tomorrow”, but were never able to fulfill on this promise due to their reliance on relational paradigm that prevent them from having the flexibility to truly provide an unlimited amount of perspectives on the same data. MDM & BI Hubs in the opposite have required all perspectives to be aligned with the one single truth that was physically incorporated in the backbone and paradigm of these respective approaches.
  7. Black current features Gray future / planned features
  8. Black current features Gray future / planned features
  9. Black current features Gray future / planned features
  10. Black current features Gray future / planned features
  11. Plattform Industrie 4.0: Gemeinschaftsprojekt der Wirtschaftsverbände BITKOM (IuK), VDMA (Maschinen/Anlagen), ZVEI (Elektro/Elektronik). Eine gleichnamige Plattform gibt’s auch in Österreich.