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Prof. Dr. Sören Auer
Knowledge Graphs Winter School
February 23rd, 2021
Introduction to
Knowledge Graphs
Page 2
About me - Prof. Dr. Sören Auer
Now: Professor for Data Science and Digital Libraries, Leibniz University of Hannover
Director TIB Leibniz Information Center for Science & Technology
• TIB is with >500 employees the largest science and technology information centre world-wide
• Strategy: organizing research data and information using knowledge graphs
• Member of the board of L3S research center – a world-leading responsible AI
Previously: U Bonn, Fraunhofer, U Leipzig, U Pennsylvania, Ural State Uni Ekaterinburg, TU Dresden
Publications in major venues: Web Conf., IJCAI, AAAI, ISWC, ESWC, K-CAP, TPDL, JWS, SWJ, JDIQ
 H-index: 57, >21.000 citations, >15 best paper awards incl. test-of-time and 10-year awards
Major scientific contributions:
• Technology platforms: OntoWiki & DBpedia, LOD2 Linked Data and BigDataEurope software stacks
• Acquisition of >20M€ for my research groups in Leipzig, Bonn and Hannover
• Strategic projects: ERC ScienceGraph, LOD2, BigDataEurope, Marie Curie ITN WDAqua
Impact & Transfer: W3C standards, 5 students now professors, successful spin-off company,
portfolio of open-source software, Int. Data Spaces Initiative, Big Data Value Association
--- 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 7
Computing more inuitive: OO programming
Sören Auer 9
Sören Auer 10
Computing even more inuitive: with cognitive data?!
Page 11
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 12
Source: Gesellschaft für
Informatik
The Three “V” of Big Data - Variety often Neglected
Page 13
Tackling the Variety Dimension
with the FAIR and Linked Data Principles
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
Page 14
1. Graph based RDF data model consisting of S-P-O statements (facts)
RDF & Linked Data in a Nutshell
WinterSchool dbpedia:
Paderborn
23.02.2021
KnowGraphs
conf:organizes
conf:starts
conf:takesPlaceIn
2. Serialised as RDF Triples:
KnowGraphs conf:organizes WinterSchool .
WinterSchool conf:starts “2021-02-23”^^xsd:date .
WinterSchool conf:takesPlaceAt dbpedia:Paderborn .
3. Publication under URL in Web, Intranet, Extranet
Subject Predicate Object
Page 15
Creating Knowledge Graphs with RDF
Linked Data
located in
label
industry
headquarters
full name
DHL
Post Tower
162.5 m
Bonn
Logistics Logistik
DHL International GmbH
height
物流
label
Page 16
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:name
dbp: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
Page 17
Knowledge Graph Example: DBpedia
• Automatically extracted
from Wikipedia infoboxes
• Crystalization point
of the LOD Cloud
https://lod-cloud.net/
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 19
Seite 20
Example: Mapping of Research Data to Ontologies
Krankheit Symptom Prävalenz
Grippe Fieber 1000
Krebs Blutung 30
... ... ...
Disease ICD-10 Symptoms Medication
Influenza J10 Fever Amantadin
Cancer C00-C97 Bleeding Chemotherapy
... ... ... ...
Symptom
Disease
ICD-10
Code
Prevalence
ICD-10
Code
Type
Drug
Name Classification
Concepts
Attributes
hasSymptom
... ... ...
hasTreatment
Vocabulary Layer
Data
Layer
Relations
Mappings
Seite 21
Example: Semantic Research Data in Engineering
Page 22
• collaborative, community activity
to create, maintain, and promote
schemas for structured data on
the Internet
• can be used with many different
encodings, including RDFa,
Microdata and JSON-LD
• covers entities, relationships
between entities and actions
• can easily be extended through a
well-documented extension model
• >10 million sites use Schema.org
to markup their web pages and
email messages
• Founded by Google, Microsoft,
Yahoo and Yandex
Vocabulary Example:
Schema.org
Die Semantic Web Layer Cake 2001
http://www.w3.org/2001/10/03-sww-1/slide7-0.html
• Monolithisch basierend auf
XML
• Fokus auf schwergewichtige
Semantik (Ontologien, Logic,
Reasoning)
The Semantic Web Layer Cake now – Bridging between Data
Unicode URIs
XML JSON CSV RDB HTML
RDF
RDF/XML JSON-LD CSV2RDF R2RML RDFa
RDF Data Shapes RDF-Schema
Vocabularies
Ontologies
SKOS Thesauri
Logic
Rules
SPARQL
(Access
control),
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”
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
25
Page 26
• 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 27
Manual
• Curation / Crowdsourcing
Markup
• schema.org
Mapping Structured Data
• R2RML/RML
Leveraging Natural Language
Processing (NLP) from text
• Named Entity Recognition
• Relation Extraction
Knowledge Graph Creation
Ignaz Wanders: Build your own Knowledge Graph: From unstructured dark
data to valuable business insights
https://medium.com/vectrconsulting/build-your-own-knowledge-graph-
975cf6dde67f
Page 28
Querying Knowledge Graphs
Graph Patterns
Corresponding SPARQL Query:
SELECT ?ev, ?vn1, ?vn2 WHERE {
?ev a Food_Festival .
?ev venue ?vn1 .
?ev venue ?vn2 .
}
A. Hogan, E. Blomqvist, M. Cochez, C. d'Amato, G. de Melo, C. Gutierrez, J. E. Labra Gayo, S. Kirrane, S.
Neumaier, A. Polleres, R. Navigli, A.-C. Ngonga Ngomo, S. M. Rashid, A. Rula, L. Schmelzeisen, J. Sequeda,
S. Staab, Antoine Zimmermann: Knowledge Graphs, arXiv:2003.02320 [cs.AI]
Page 29
Knowledge Graph Reasoning
Reveals implicit information
Page 30
Knowledge Graph Refinement
Completion
• Filling missing edges
• Often addressed with link prediction
• Special tasks: type and identity prediction
Correction
• Fact validation
• Inconsistency repair
Page 31
Knowledge Graph Quality
[1] Zaveri, Rula, Maurino, Auer, Lehmann: Quality Assessment for Linked Open Data. Semantic Web Journal, 2015
A1: server responds to a SPARQL query
A2: RDF dump is available
A3: detection of dereferenceability of URIs
A4: HTTP response header with appropriate content type
A5: dereferenceability of all forward links
CM1: schema completeness: ratio of represented classes/properties
CM2: property completeness
CM3: population completeness: ratio of real-world objects
CM4: interlinking completeness: ratio of interlinked instances
Data quality is
“fitness for use”
Use cases vary 
various quality
criteria/measures
organized along
various dimensions
Page 32
Page 33
Instances in DBpedia & Wikidata
Knowledge Graphs on the Web -- an Overview
N. Heist, S. Hertling, D. Ringler, H. Paulheim
Page 34
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
Page 35
Initiatives for decentral, semantic data spaces
Web/Ecommerce Digital Libraries Life Sciences Industry Open
Government Data
Vocabularies schema.org Europeana Data
Model
DCAT, DC, PROV-
O, FOAF, VoiD
DCAT, IDS
Vocabulary
DCAT
Participants ~30% of Web pages Memory Institutions
(2000 in Germany)
Pharma companies 80 companies
(SAP, Siemens,
Telekom, PWC)
EU, Countries,
Cities, Counties
License
Governance
CC-BY-SA
GitHub,
Google, Microsoft, Ya
ndex...
CC0
Europeana
Association
CC-BY-SA IDS Association Open Data
Applications Google Knowledge
Graph (Produkte,
Personen, ...)
DDB.de,
Europeana.eu
OpenPhacts.org Industrial Data
Space
Transparency,
Mobility, Budget,
Planing
Page 36
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 37
Industry
Knowledge
Graph
Adoption
https://www.slideshare.net/
Frank.van.Harmelen/adopti
on-of-knowledge-graphs-
late-2019
Eccenca aims at making
KGs a commodity
Page 38
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
exchange (data value chains)
38
Knowledge Graphs on the Web -- an Overview
N. Heist, S. Hertling, D. Ringler, H. Paulheim
DBpedia YAGO WikiData BabelNet
Cyc NELL CaLiGraph Voldemort
Page 39
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] M. Galkin, S. Auer, M.-E. Vidal, S. Scerri: Enterprise Knowledge Graphs: A Semantic Approach for Knowledge
Management in the Next Generation of Enterprise Information Systems. ICEIS (2) 2017: 88-98
Page 40
Knowledge Graph Technology
4
Example
Enterprise Data Integration
with A Semantic Data Lake
Page 42
Perspectives on data turn into silos
Parts of data are being curated, duplicated, annotated and simply
changed over time, making reconciliation and interpretation a challenge
Engineering Manufactur. Logistics Marketing
. . .
Page 43
Integrate Using RDF & Vocabularies
Engineering Manufactur. Logistics Marketing
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
Knowledge Graph based Enterprise Data
Innovation Architecture
The future of data management is semantic!
Enterprise Integration with a
Semantic Data Lake
The Problem today
Management
Accounting
Risk Management
Regulatory Reporting
Treasury Marketing
Accounting
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-
Infrastructur
e
High Level Architecture
Corporate Memory
Interlinking/
Fusing
Classification
/ Enrichment
Quality
Analysis
Evolution
/ Repair
Search/
Browsing/
Exploration
Extraction
Storage/
Querying
Manual
revision/
authorin
g
Covering the Linked Data Life Cycle
• Extraction / Mapping
• Storage / Querying
• Manual Revision / Authoring
• Linking / Fusion
• Classification / Enrichment
• Quality / Evolution
• Search / Browse / Explore
Triple/Quad
Store
Backend
RDB2RDF
UI Framework
Repair
UI Framework
DataPlatform
Data Manager
Data Integration
Ontology
Learner
DataPlatform
RDB2RDF
DataManager
NLP
Core eccenca USPs:
• Provenance
Tracking
• Graph Replication
• Data Mapping
• Versioning
• Access Control
© eccenca GmbH 2018
© eccenca GmbH 2018
Organizing Scholarly
Communication with
Knowledge Graphs
Page 49
How did information flows change
in the digital era?
Page 50
How does it work today?
The World of Publishing &
Communication has profundely changed
• New means adapted to the new possibilities were
developed, e.g. „zooming“, dynamics
• Business models changed completely
• More focus on data, interlinking of data / services and
search in the data
• Integration, crowdsourcing, data curation play an
important role
Page 51
What about
Scholarly
Communication?
Page 52
Scholarly Communication has not changed
(much)
17th century 19th century 20th century 21th century
Meanwhile other information intense domains were
completely disrupted:
Page 53
Challenges we are facing:
We need to rethink the way how research
is represented and communicated
[1] http://thecostofknowledge.com, https://www.projekt-deal.de
[2] M. Baker: 1,500 scientists lift the lid on reproducibility, Nature, 2016.
[3] Science and Engineering Publication Output Trends, National Science Foundation, 2018.
[4] J. Couzin-Frankel: Secretive and Subjective, Peer Review Proves Resistant to Study. Science, 2013.
Digitalisation
of Science
 Data integration
and analysis
 Digital
collaboration
Monopolisation by
commercial actors
 Publisher
look-in effects
 Maximization
of profits [1]
Reproducibility
Crisis
 Majority of
experiments are
hard or not
reproducible [2]
Proliferation
of publications
 Publication output
doubled within a
decade
 continues to rise
[3]
Deficiency
of Peer Review
 Deteriorating
quality [4]
 Predatory
publishing
Page 54
Lack of…
Root Cause –
Deficiency of Scholarly Communication?
Transparency
information is hidden
in text
Integratability
fitting different
research results
together
Machine assistance
unstructured content
is hard to process
Identifyability
of concepts beyond
metadata
Collaboration
one brain barrier
Overview
Scientists look for the
needle in the haystack
Page 55
How good is CRISPR
(wrt. precision, safety, cost)?
What specifics has genome
editing with insects?
Who has applied it to
butterflies?
Search for CRISPR:
> 238.000 Results
Source: https://scholar.google.de/scholar?hl=de&as_sdt=0%2C5&q=CRISPR&btnG=, 04.2019
Page 56
How can
we fix it?
Page 57
Mathematics
• Definitions
• Theorems
• Proofs
• Methods
• …
Physics
• Experiments
• Data
• Models
• …
Chemistry
• Substances
• Structures
• Reactions
• …
Computer
Science
• Concepts
• Implemen-
tations
• Evaluations
• …
Technology
• Standards
• Processes
• Elements
• Units,
Sensor data
Architecture
• Regulations
• Elements
• Models
• …
Concepts
Overarching Concepts
 Research problems
 Definitions
 Research approaches
 Methods
Artefacts
 Publications
 Data
 Software
 Image/Audio/Video
 Knowledge Graphs / Ontologies
Domain specific Concepts
Page 58
KGs are proven to capture factual knowledge
Research Challenge: Manage
• Uncertainty & disagreement
• Varying semantic granularity
• Emergence, evolution & provenance
• Integrating existing domain models
But maintain flexibility and simplicity
Cognitive Knowledge Graphs
for scholarly knowledge
Towards Cognitive
Knowledge Graphs
• Fabric of knowledge molecules – compact,
relatively simple, structured units of knowledge
• Can be incrementally enriched, annotated, interlinked …
Page 59
Factual
Base entities Real world
Granularity Atomic Entities
Evolution
Addition/deletion
of facts
Collaboration Fact enrichment
From Factual Knowledge Graphs
Today
Page 60
Factual Cognitive
Base entities Real world Conceptual
Granularity Atomic Entities
Interlinked descriptions (molecules)
with annotations (provenance)
Evolution
Addition/deletion
of facts
Concept drift,
varying aggregation levels
Collaboration Fact enrichment Emergent semantics
From Factual to Cognitive Knowledge Graphs
Today Needed for SKG
Page 61
Chemistry Example: CRISPR Genome Editing
Source: https://cacm.acm.org/system/assets/0002/2618/021116_Google_KnowledgeGraph.large.jpg?1476779500&1455222197
Page 62
1. Original Publication
Chemistry Example: Populating the Graph
2. Adaptive Graph Curation & Completion
Author Robert Reed
Research Problem Genome editing in Lepidoptera
Methods CRISPR / cas9
Applied on Lepidoptera
Experimental Data
https://doi.org/10.5281/zenodo.89691
6
3. Graph representation
CRISPR / cas9 editing
in Lepidoptera
https://doi.org/10.1101/130344
Robert Reed
https://orcid.org/0000-0002-6065-6728
Genome editing in
Lepidoptera
Experimental Data
https://doi.org/10.5281/zenodo.896916
adresses
CRSPRS/cas9
isEvaluatedWith
Genome editing
https://www.wikidata.org/wiki/Q24630389
Page 63
Research Challenge:
• Intuitive exploration leveraging the
rich semantic representations
• Answer natural language questions
Exploration and Question Answering
Questi
on
parsin
g Named
Entity
Recogniti
on (NER)
& Linking
(NEL)
Relatio
n
extracti
on
Query
con-
structi
on
Query
executi
on
Result
renderi
ng
Q: How do different
genome editing techniques
compare?
SELECT Approach, Feature WHERE {
Approach adresses GenomEditing .
Approach hasFeature Feature }
[1] K. Singh, S. Auer et al: Why Reinvent
the Wheel? Let's Build Question
Answering Systems Together. The Web
Conference (WWW 2018).
Q: How do different
genome editing techniques
compare?
Page 64
Engineered Nucleases Site-specificity Safety Ease-of-use / costs/ speed
zinc finger nucleases (ZFN) ++
9-18nt
+ --
$$$: screening, testing to define efficiency
transcription activator-like
effector nucleases (TALENs)
+++
9-16nt
++ ++
Easy to engineer
1 week / few hundred dollar
engineered meganucleases +++
12-40 nt
0 --
$$$ Protein engineering, high-throughput
screening
CRISPR system/cas9 ++
5-12 nt
- +++
Easy to engineer
few days / less 200 dollar
Result:
Automatic Generation of Comparisons / Surveys
Q: How do different genome editing techniques
compare?
Conclusion
Page 72
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
Knowledge Graphs can make a difference
Page 73
The Team
Prof. (Univ. S. Bolivar)
Dr. Maria Esther Vidal
Software Development
Dr. Kemele Endris
Collaborators TIB Scientific Data Mgmt.
Group Leaders PostDocs
Project Management
Doctoral Researchers
Dr. Markus Stocker Dr. Gábor Kismihók Dr. Javad Chamanara Dr. Jennifer D’Souza
Allard Oelen Yaser Jaradeh Manuel Prinz
Alex Garatzogianni
Collaborators InfAI Leipzig / AKSW
Dr. Michael Martin Natanael Arndt
Dr. Lars Vogt
Vitalis Wiens Kheir Eddine Farfar
Muhammad Haris
Administration
Katja Bartel Simone Matern
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

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Knowledge Graph Introduction

  • 1. Prof. Dr. Sören Auer Knowledge Graphs Winter School February 23rd, 2021 Introduction to Knowledge Graphs
  • 2. Page 2 About me - Prof. Dr. Sören Auer Now: Professor for Data Science and Digital Libraries, Leibniz University of Hannover Director TIB Leibniz Information Center for Science & Technology • TIB is with >500 employees the largest science and technology information centre world-wide • Strategy: organizing research data and information using knowledge graphs • Member of the board of L3S research center – a world-leading responsible AI Previously: U Bonn, Fraunhofer, U Leipzig, U Pennsylvania, Ural State Uni Ekaterinburg, TU Dresden Publications in major venues: Web Conf., IJCAI, AAAI, ISWC, ESWC, K-CAP, TPDL, JWS, SWJ, JDIQ  H-index: 57, >21.000 citations, >15 best paper awards incl. test-of-time and 10-year awards Major scientific contributions: • Technology platforms: OntoWiki & DBpedia, LOD2 Linked Data and BigDataEurope software stacks • Acquisition of >20M€ for my research groups in Leipzig, Bonn and Hannover • Strategic projects: ERC ScienceGraph, LOD2, BigDataEurope, Marie Curie ITN WDAqua Impact & Transfer: W3C standards, 5 students now professors, successful spin-off company, portfolio of open-source software, Int. Data Spaces Initiative, Big Data Value Association
  • 3. --- VERTRAULICH --- Zuse Z3: the beginning of Computing – close to the hardware Foto: Konrad Zuse Internet Archiv/Deutsches Museum/DFG
  • 5. --- 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/
  • 6. Computing more inuitive: procedural programming
  • 8. Computing more inuitive: OO programming
  • 10. Sören Auer 10 Computing even more inuitive: with cognitive data?!
  • 11. Page 11 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!
  • 12. Page 12 Source: Gesellschaft für Informatik The Three “V” of Big Data - Variety often Neglected
  • 13. Page 13 Tackling the Variety Dimension with the FAIR and Linked Data Principles 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
  • 14. Page 14 1. Graph based RDF data model consisting of S-P-O statements (facts) RDF & Linked Data in a Nutshell WinterSchool dbpedia: Paderborn 23.02.2021 KnowGraphs conf:organizes conf:starts conf:takesPlaceIn 2. Serialised as RDF Triples: KnowGraphs conf:organizes WinterSchool . WinterSchool conf:starts “2021-02-23”^^xsd:date . WinterSchool conf:takesPlaceAt dbpedia:Paderborn . 3. Publication under URL in Web, Intranet, Extranet Subject Predicate Object
  • 15. Page 15 Creating Knowledge Graphs with RDF Linked Data located in label industry headquarters full name DHL Post Tower 162.5 m Bonn Logistics Logistik DHL International GmbH height 物流 label
  • 16. Page 16 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:name dbp: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
  • 17. Page 17 Knowledge Graph Example: DBpedia • Automatically extracted from Wikipedia infoboxes • Crystalization point of the LOD Cloud https://lod-cloud.net/
  • 18. 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
  • 19. 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 19
  • 20. Seite 20 Example: Mapping of Research Data to Ontologies Krankheit Symptom Prävalenz Grippe Fieber 1000 Krebs Blutung 30 ... ... ... Disease ICD-10 Symptoms Medication Influenza J10 Fever Amantadin Cancer C00-C97 Bleeding Chemotherapy ... ... ... ... Symptom Disease ICD-10 Code Prevalence ICD-10 Code Type Drug Name Classification Concepts Attributes hasSymptom ... ... ... hasTreatment Vocabulary Layer Data Layer Relations Mappings
  • 21. Seite 21 Example: Semantic Research Data in Engineering
  • 22. Page 22 • collaborative, community activity to create, maintain, and promote schemas for structured data on the Internet • can be used with many different encodings, including RDFa, Microdata and JSON-LD • covers entities, relationships between entities and actions • can easily be extended through a well-documented extension model • >10 million sites use Schema.org to markup their web pages and email messages • Founded by Google, Microsoft, Yahoo and Yandex Vocabulary Example: Schema.org
  • 23. Die Semantic Web Layer Cake 2001 http://www.w3.org/2001/10/03-sww-1/slide7-0.html • Monolithisch basierend auf XML • Fokus auf schwergewichtige Semantik (Ontologien, Logic, Reasoning)
  • 24. The Semantic Web Layer Cake now – Bridging between Data Unicode URIs XML JSON CSV RDB HTML RDF RDF/XML JSON-LD CSV2RDF R2RML RDFa RDF Data Shapes RDF-Schema Vocabularies Ontologies SKOS Thesauri Logic Rules SPARQL (Access control), 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”
  • 25. 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 25
  • 26. Page 26 • 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
  • 27. Page 27 Manual • Curation / Crowdsourcing Markup • schema.org Mapping Structured Data • R2RML/RML Leveraging Natural Language Processing (NLP) from text • Named Entity Recognition • Relation Extraction Knowledge Graph Creation Ignaz Wanders: Build your own Knowledge Graph: From unstructured dark data to valuable business insights https://medium.com/vectrconsulting/build-your-own-knowledge-graph- 975cf6dde67f
  • 28. Page 28 Querying Knowledge Graphs Graph Patterns Corresponding SPARQL Query: SELECT ?ev, ?vn1, ?vn2 WHERE { ?ev a Food_Festival . ?ev venue ?vn1 . ?ev venue ?vn2 . } A. Hogan, E. Blomqvist, M. Cochez, C. d'Amato, G. de Melo, C. Gutierrez, J. E. Labra Gayo, S. Kirrane, S. Neumaier, A. Polleres, R. Navigli, A.-C. Ngonga Ngomo, S. M. Rashid, A. Rula, L. Schmelzeisen, J. Sequeda, S. Staab, Antoine Zimmermann: Knowledge Graphs, arXiv:2003.02320 [cs.AI]
  • 29. Page 29 Knowledge Graph Reasoning Reveals implicit information
  • 30. Page 30 Knowledge Graph Refinement Completion • Filling missing edges • Often addressed with link prediction • Special tasks: type and identity prediction Correction • Fact validation • Inconsistency repair
  • 31. Page 31 Knowledge Graph Quality [1] Zaveri, Rula, Maurino, Auer, Lehmann: Quality Assessment for Linked Open Data. Semantic Web Journal, 2015 A1: server responds to a SPARQL query A2: RDF dump is available A3: detection of dereferenceability of URIs A4: HTTP response header with appropriate content type A5: dereferenceability of all forward links CM1: schema completeness: ratio of represented classes/properties CM2: property completeness CM3: population completeness: ratio of real-world objects CM4: interlinking completeness: ratio of interlinked instances Data quality is “fitness for use” Use cases vary  various quality criteria/measures organized along various dimensions
  • 33. Page 33 Instances in DBpedia & Wikidata Knowledge Graphs on the Web -- an Overview N. Heist, S. Hertling, D. Ringler, H. Paulheim
  • 34. Page 34 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
  • 35. Page 35 Initiatives for decentral, semantic data spaces Web/Ecommerce Digital Libraries Life Sciences Industry Open Government Data Vocabularies schema.org Europeana Data Model DCAT, DC, PROV- O, FOAF, VoiD DCAT, IDS Vocabulary DCAT Participants ~30% of Web pages Memory Institutions (2000 in Germany) Pharma companies 80 companies (SAP, Siemens, Telekom, PWC) EU, Countries, Cities, Counties License Governance CC-BY-SA GitHub, Google, Microsoft, Ya ndex... CC0 Europeana Association CC-BY-SA IDS Association Open Data Applications Google Knowledge Graph (Produkte, Personen, ...) DDB.de, Europeana.eu OpenPhacts.org Industrial Data Space Transparency, Mobility, Budget, Planing
  • 36. Page 36 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
  • 38. Page 38 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 exchange (data value chains) 38 Knowledge Graphs on the Web -- an Overview N. Heist, S. Hertling, D. Ringler, H. Paulheim DBpedia YAGO WikiData BabelNet Cyc NELL CaLiGraph Voldemort
  • 39. Page 39 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] M. Galkin, S. Auer, M.-E. Vidal, S. Scerri: Enterprise Knowledge Graphs: A Semantic Approach for Knowledge Management in the Next Generation of Enterprise Information Systems. ICEIS (2) 2017: 88-98
  • 40. Page 40 Knowledge Graph Technology 4
  • 42. Page 42 Perspectives on data turn into silos Parts of data are being curated, duplicated, annotated and simply changed over time, making reconciliation and interpretation a challenge Engineering Manufactur. Logistics Marketing . . .
  • 43. Page 43 Integrate Using RDF & Vocabularies Engineering Manufactur. Logistics Marketing
  • 44. 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 Knowledge Graph based Enterprise Data Innovation Architecture The future of data management is semantic! Enterprise Integration with a Semantic Data Lake The Problem today
  • 45. Management Accounting Risk Management Regulatory Reporting Treasury Marketing Accounting 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- Infrastructur e High Level Architecture Corporate Memory
  • 46. Interlinking/ Fusing Classification / Enrichment Quality Analysis Evolution / Repair Search/ Browsing/ Exploration Extraction Storage/ Querying Manual revision/ authorin g Covering the Linked Data Life Cycle • Extraction / Mapping • Storage / Querying • Manual Revision / Authoring • Linking / Fusion • Classification / Enrichment • Quality / Evolution • Search / Browse / Explore Triple/Quad Store Backend RDB2RDF UI Framework Repair UI Framework DataPlatform Data Manager Data Integration Ontology Learner DataPlatform RDB2RDF DataManager NLP Core eccenca USPs: • Provenance Tracking • Graph Replication • Data Mapping • Versioning • Access Control © eccenca GmbH 2018
  • 49. Page 49 How did information flows change in the digital era?
  • 50. Page 50 How does it work today? The World of Publishing & Communication has profundely changed • New means adapted to the new possibilities were developed, e.g. „zooming“, dynamics • Business models changed completely • More focus on data, interlinking of data / services and search in the data • Integration, crowdsourcing, data curation play an important role
  • 52. Page 52 Scholarly Communication has not changed (much) 17th century 19th century 20th century 21th century Meanwhile other information intense domains were completely disrupted:
  • 53. Page 53 Challenges we are facing: We need to rethink the way how research is represented and communicated [1] http://thecostofknowledge.com, https://www.projekt-deal.de [2] M. Baker: 1,500 scientists lift the lid on reproducibility, Nature, 2016. [3] Science and Engineering Publication Output Trends, National Science Foundation, 2018. [4] J. Couzin-Frankel: Secretive and Subjective, Peer Review Proves Resistant to Study. Science, 2013. Digitalisation of Science  Data integration and analysis  Digital collaboration Monopolisation by commercial actors  Publisher look-in effects  Maximization of profits [1] Reproducibility Crisis  Majority of experiments are hard or not reproducible [2] Proliferation of publications  Publication output doubled within a decade  continues to rise [3] Deficiency of Peer Review  Deteriorating quality [4]  Predatory publishing
  • 54. Page 54 Lack of… Root Cause – Deficiency of Scholarly Communication? Transparency information is hidden in text Integratability fitting different research results together Machine assistance unstructured content is hard to process Identifyability of concepts beyond metadata Collaboration one brain barrier Overview Scientists look for the needle in the haystack
  • 55. Page 55 How good is CRISPR (wrt. precision, safety, cost)? What specifics has genome editing with insects? Who has applied it to butterflies? Search for CRISPR: > 238.000 Results Source: https://scholar.google.de/scholar?hl=de&as_sdt=0%2C5&q=CRISPR&btnG=, 04.2019
  • 57. Page 57 Mathematics • Definitions • Theorems • Proofs • Methods • … Physics • Experiments • Data • Models • … Chemistry • Substances • Structures • Reactions • … Computer Science • Concepts • Implemen- tations • Evaluations • … Technology • Standards • Processes • Elements • Units, Sensor data Architecture • Regulations • Elements • Models • … Concepts Overarching Concepts  Research problems  Definitions  Research approaches  Methods Artefacts  Publications  Data  Software  Image/Audio/Video  Knowledge Graphs / Ontologies Domain specific Concepts
  • 58. Page 58 KGs are proven to capture factual knowledge Research Challenge: Manage • Uncertainty & disagreement • Varying semantic granularity • Emergence, evolution & provenance • Integrating existing domain models But maintain flexibility and simplicity Cognitive Knowledge Graphs for scholarly knowledge Towards Cognitive Knowledge Graphs • Fabric of knowledge molecules – compact, relatively simple, structured units of knowledge • Can be incrementally enriched, annotated, interlinked …
  • 59. Page 59 Factual Base entities Real world Granularity Atomic Entities Evolution Addition/deletion of facts Collaboration Fact enrichment From Factual Knowledge Graphs Today
  • 60. Page 60 Factual Cognitive Base entities Real world Conceptual Granularity Atomic Entities Interlinked descriptions (molecules) with annotations (provenance) Evolution Addition/deletion of facts Concept drift, varying aggregation levels Collaboration Fact enrichment Emergent semantics From Factual to Cognitive Knowledge Graphs Today Needed for SKG
  • 61. Page 61 Chemistry Example: CRISPR Genome Editing Source: https://cacm.acm.org/system/assets/0002/2618/021116_Google_KnowledgeGraph.large.jpg?1476779500&1455222197
  • 62. Page 62 1. Original Publication Chemistry Example: Populating the Graph 2. Adaptive Graph Curation & Completion Author Robert Reed Research Problem Genome editing in Lepidoptera Methods CRISPR / cas9 Applied on Lepidoptera Experimental Data https://doi.org/10.5281/zenodo.89691 6 3. Graph representation CRISPR / cas9 editing in Lepidoptera https://doi.org/10.1101/130344 Robert Reed https://orcid.org/0000-0002-6065-6728 Genome editing in Lepidoptera Experimental Data https://doi.org/10.5281/zenodo.896916 adresses CRSPRS/cas9 isEvaluatedWith Genome editing https://www.wikidata.org/wiki/Q24630389
  • 63. Page 63 Research Challenge: • Intuitive exploration leveraging the rich semantic representations • Answer natural language questions Exploration and Question Answering Questi on parsin g Named Entity Recogniti on (NER) & Linking (NEL) Relatio n extracti on Query con- structi on Query executi on Result renderi ng Q: How do different genome editing techniques compare? SELECT Approach, Feature WHERE { Approach adresses GenomEditing . Approach hasFeature Feature } [1] K. Singh, S. Auer et al: Why Reinvent the Wheel? Let's Build Question Answering Systems Together. The Web Conference (WWW 2018). Q: How do different genome editing techniques compare?
  • 64. Page 64 Engineered Nucleases Site-specificity Safety Ease-of-use / costs/ speed zinc finger nucleases (ZFN) ++ 9-18nt + -- $$$: screening, testing to define efficiency transcription activator-like effector nucleases (TALENs) +++ 9-16nt ++ ++ Easy to engineer 1 week / few hundred dollar engineered meganucleases +++ 12-40 nt 0 -- $$$ Protein engineering, high-throughput screening CRISPR system/cas9 ++ 5-12 nt - +++ Easy to engineer few days / less 200 dollar Result: Automatic Generation of Comparisons / Surveys Q: How do different genome editing techniques compare?
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  • 72. Page 72 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 Knowledge Graphs can make a difference
  • 73. Page 73 The Team Prof. (Univ. S. Bolivar) Dr. Maria Esther Vidal Software Development Dr. Kemele Endris Collaborators TIB Scientific Data Mgmt. Group Leaders PostDocs Project Management Doctoral Researchers Dr. Markus Stocker Dr. Gábor Kismihók Dr. Javad Chamanara Dr. Jennifer D’Souza Allard Oelen Yaser Jaradeh Manuel Prinz Alex Garatzogianni Collaborators InfAI Leipzig / AKSW Dr. Michael Martin Natanael Arndt Dr. Lars Vogt Vitalis Wiens Kheir Eddine Farfar Muhammad Haris Administration Katja Bartel Simone Matern

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. 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.
  6. Kemele M. Endris, Mikhail Galkin, Ioanna Lytra, Mohamed Nadjib Mami, Maria-Esther Vidal, Sören Auer: MULDER: Querying the Linked Data Web by Bridging RDF Molecule Templates. DEXA (1) 2017: 3-18
  7. D. Diefenbach, K. Singh, A. Both, D. Cherix, C. Lange, S. Auer. 2017. The Qanary Ecosystem: Getting New Insights by Composing Question Answering Pipelines. Int. Conf. on Web Engineering ICWE 2017. K. Singh, A. Sethupat, A. Both, S. Shekarpour, I. Lytra, R. Usbeck, A. Vyas, A. Khikmatullaev, D. Punjani, C. Lange, M.-E. Vidal, J. Lehmann, S. Auer: Why Reinvent the Wheel-Let's Build Question Answering Systems Together. The Web Conference (WWW 2018). S. Shekarpour, E. Marx, S. Auer, A. P. Sheth: RQUERY: Rewriting Natural Language Queries on Knowledge Graphs to Alleviate the Vocabulary Mismatch Problem. AAAI 2017: 3936-3943 D. Lukovnikov, A. Fischer, J. Lehmann, S. Auer: Neural Network-based Question Answering over Knowledge Graphs on Word and Character Level. WWW 2017: 1211-1220