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Sören Auer
Symposium of the Knowledge Graph IG at the
Alan Turing Institute
June 17, 2022
Knowledge Graph Research and
Innovation Challenges
Page 2
• 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
Page 3
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 4
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
KGs are pretty much
established for Data
Integration, but what
about real Knowledge?
Page 5
1. Integrate KGs with ML - Neuro-symbolic AI
2. Extend the concept of KGs
3. Establish true Human-Machine Collaboration
From KGs for Data Integration to KGs for
Knowledge Integration
Integrate KGs with ML -
Neuro-symbolic AI
Page 7
How can we combine ML and KG?
ML reseracher: We can learn on graphs (GNN) 
KG researcher: We can use ML for KG completion (KG embedding) 
Page 8
Towards Neuro-Symbolic Perception
Input Output
Horse
Tail
4
hasLegs
has
Pony small
size
subClassOf
Zebra Stripes
has
subClassOf
Page 9
What do we need?
1. Use KGs as contextual/background knowledge for ML in addition to
raw data  Causal reasoning
2. Use ML to extend and revise KGs
3. Integrate human and machine intelligence
Page 10
Synergistic Combination of Human & Machine
Intelligence leveraging Knowledge Graphs
Machine Intelligence
Cognitive
Knowledge Graph Human Intelligence
Concept
KG nodes/graphlets
Connecting KG graphlets
with ML models
KG graphlet authoring,
curation, validation
Extend the concept of KGs
Page 12
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 (graphlets) –
compact, relatively simple, structured units of knowledge
• Can be incrementally enriched, annotated, interlinked …
Page 13
KG Graphlets initial working definition
Formally a CKG graphlet is a tuple of sets of classes and properties (C,P), where
1. ∀ p ∈ P the domain (either explicitly defined or implicitly inferred from a concrete
CKG) includes at least one of the types c ∈ C: domain(p) ⊂ C and
2. all classes in C are connected via a property chain in P: ∀c1, c2 ∈ C ∃p1, ..., pj, ..., pn
∈ P: domain(p1) = c1, range(pj) = domain(pj+1), range(pn) = c2.
Alternatively (a) a special type of connected graph patterns, where variables occur in the
positions of concrete instances and literals or (b) as specific sets of SHACL shapes.
Graphlets can serve as a structuring element between entity/resource descriptions
and whole ontologies/KGs  KG management (e.g. reasoning, querying,
completion etc.) can be adapted to KG graphlet handling
Page 14
Graphlet Example „Scholarly Contribution“
Page 15
Graphlet Example „Secutiry Advise“
Page 16
Factual
Base entities Real world
Granularity Atomic Entities
Evolution
Addition/deletion
of facts
Collaboration Fact enrichment
From Factual Knowledge Graphs
Today
Page 17
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
Organizing Scholarly
Communication with
Knowledge Graphs
Page 19
How did information flows change
in the digital era?
Page 20
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 21
What about
Scholarly
Communication?
Page 22
Scholarly Communication has not changed
(much)
17th century 19th century 20th century 21th century
Page 23
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 24
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 25
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 26
How can
we fix it?
Page 27
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 28
Chemistry Example: CRISPR Genome Editing
Source: https://cacm.acm.org/system/assets/0002/2618/021116_Google_KnowledgeGraph.large.jpg?1476779500&1455222197
Page 29
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 30
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 31
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?
Page 32
The Open Research
Knowledge Graph
Establish true Human-
Machine Collaboration
To create a scholarly knowledge graph, a transformation from unstructured
to structured knowledge should happen
ORKG | Knowledge transformation
Unstructured knowledge Structured knowledge
Can we use Natural Language Processing (NLP) for
the transformation process?
● NLP techniques are not sufficiently accurate to perform this task
autonomously
● But we can intertwine machine intelligence with human intelligence
to get a synergy → the best of both worlds!
ORKG | Knowledge transformation
Can we use Natural Language Processing (NLP) for
the transformation process?
74% 84% 78%
x x = 48% Error propagation
Manual data entry
Gradations of automation
Human-in-the-loop
Machine-in-the-loop Fully automated
Human adds
paper manually
Human is assisted
by a machine
Assistance Assistance
Machine is assisted
by a human
Machine adds paper
automatically
Better scalable
Manual data entry
Gradations of automation
Human-in-the-loop
Machine-in-the-loop Fully automated
Human adds
paper manually
Human is assisted
by a machine
Assistance Assistance
Machine is assisted
by a human
Machine adds paper
automatically
Better scalable
Human-in-the-loop
Machine-in-the-loop
Human is assisted
by a machine
Assistance Assistance
Machine is assisted
by a human
Gradations of automation
Human-in-the-loop
Machine-in-the-loop Human-in-the-loop
Machine-in-the-loop
1. Add paper wizard
2. Paper
annotator
3. TinyGenius
Main entry point of adding new
papers to the ORKG
Annotation of key sentences in
scholarly PDF articles
Microtasks to validate NLP
generated statements
Gradations of automation
Human-in-the-loop
Machine-in-the-loop Human-in-the-loop
Machine-in-the-loop
1. Add paper wizard
2. Paper
annotator
3. TinyGenius
Main entry point of adding new
papers to the ORKG
Annotation of key sentences in
scholarly PDF articles
Microtasks to validate NLP
generated statements
Machine-in-the-loop | Add paper wizard | Step 1
● Collect metadata of
paper
● Fetched
automatically if a
DOI is available
● Manual entry
possible
Machine-in-the-loop | Add paper wizard | Step 2
● Selection of a
research field
● Shows the ORKG
research field
taxonomy
Machine-in-the-loop | Add paper wizard | Step 3
The third step is the
description of
contribution data
Machine-in-the-
loop
Add paper wizard - Step 3
● The third step is the
description of
contribution data
● This includes the
possibility to
annotate the
abstract
● The user is in charge and
make the final decision on
whether the automatically
generated data is added on not
(i.e., machine-in-the-loop)
● Annotations can be added or
removed
● A confidence slider hides
suggestions with a low score
Machine-in-the-loop | Add paper wizard
Try it yourself!
https://www.orkg.org/orkg/add-paper
Gradations of automation
Human-in-the-loop
Machine-in-the-loop Human-in-the-loop
Machine-in-the-loop
1. Add paper wizard
2. Paper
annotator
3. TinyGenius
Main entry point of adding new
papers to the ORKG
Annotation of key sentences in
scholarly PDF articles
Microtasks to validate NLP
generated statements
Machine-in-the-loop | Paper annotator
● Goal: annotate key sentences
in scholarly articles with
discourse classes
● Two machine-in-the-loop
approaches: sentence
highlighting and class
recommendations
Sentence highlighting
● Highlights potentially interesting sentences within
the article
● Can be ignored by users
Class recommendations
Recommends potentially relevant classes based on the
selected sentence, called “Smart suggestions”
Machine-in-the-loop | Add paper wizard
Try it yourself!
https://www.orkg.org/orkg/pdf-text-annotation
● The human takes the lead, machine assists where possible
● The user interface integration plays a key role
● Machine provides non-intrusive suggestions, wrong suggestions can
easily be ignored
● Indicate to users that suggestions are based on AI (for example by
using a dedicated color schema)
Machine-in-the-loop takeaways
Gradations of automation
Human-in-the-loop
Machine-in-the-loop Human-in-the-loop
Machine-in-the-loop
1. Add paper wizard
2. Paper
annotator
3. TinyGenius
Main entry point of adding new
papers to the ORKG
Annotation of key sentences in
scholarly PDF articles
Microtasks to validate NLP
generated statements
● Leverage existing NLP tools to process large quantities of scholarly
data
● Ask any user/visitor to validate the statements using simple tasks (aka
microtasks)
● Users that are normally “content consumers” can become
“content creators” as microtasks lower the entrance barrier to
contribute significantly
Human-in-the-loop | TinyGenius
● Use question templates to ask relevant questions for a variety of NLP
tasks
Summarization (Hugging face)
Entity Linking (Ambiverse NLU)
Open Information Extraction (ORKG abstract annotator & ORKG title parser)
Topic Modeling (CSO Classifier)
Human-in-the-loop | TinyGenius | NLP tasks
Show only validated statements by default
Human-in-the-loop | TinyGenius | Prototype
Conclusion
Page 63
1. Neuro Symbolic AI – combination of knowledge graphs and machine learning
2. Extend the concept of KGs (e.g. with graphlets)
3. Integration of Human and Machine Intelligence (e.g. with crowdsourcing)
The grand KG challenges
Page 64
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 Research and Innovation Challenges

  • 1. Sören Auer Symposium of the Knowledge Graph IG at the Alan Turing Institute June 17, 2022 Knowledge Graph Research and Innovation Challenges
  • 2. Page 2 • 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
  • 4. Page 4 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 KGs are pretty much established for Data Integration, but what about real Knowledge?
  • 5. Page 5 1. Integrate KGs with ML - Neuro-symbolic AI 2. Extend the concept of KGs 3. Establish true Human-Machine Collaboration From KGs for Data Integration to KGs for Knowledge Integration
  • 6. Integrate KGs with ML - Neuro-symbolic AI
  • 7. Page 7 How can we combine ML and KG? ML reseracher: We can learn on graphs (GNN)  KG researcher: We can use ML for KG completion (KG embedding) 
  • 8. Page 8 Towards Neuro-Symbolic Perception Input Output Horse Tail 4 hasLegs has Pony small size subClassOf Zebra Stripes has subClassOf
  • 9. Page 9 What do we need? 1. Use KGs as contextual/background knowledge for ML in addition to raw data  Causal reasoning 2. Use ML to extend and revise KGs 3. Integrate human and machine intelligence
  • 10. Page 10 Synergistic Combination of Human & Machine Intelligence leveraging Knowledge Graphs Machine Intelligence Cognitive Knowledge Graph Human Intelligence Concept KG nodes/graphlets Connecting KG graphlets with ML models KG graphlet authoring, curation, validation
  • 12. Page 12 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 (graphlets) – compact, relatively simple, structured units of knowledge • Can be incrementally enriched, annotated, interlinked …
  • 13. Page 13 KG Graphlets initial working definition Formally a CKG graphlet is a tuple of sets of classes and properties (C,P), where 1. ∀ p ∈ P the domain (either explicitly defined or implicitly inferred from a concrete CKG) includes at least one of the types c ∈ C: domain(p) ⊂ C and 2. all classes in C are connected via a property chain in P: ∀c1, c2 ∈ C ∃p1, ..., pj, ..., pn ∈ P: domain(p1) = c1, range(pj) = domain(pj+1), range(pn) = c2. Alternatively (a) a special type of connected graph patterns, where variables occur in the positions of concrete instances and literals or (b) as specific sets of SHACL shapes. Graphlets can serve as a structuring element between entity/resource descriptions and whole ontologies/KGs  KG management (e.g. reasoning, querying, completion etc.) can be adapted to KG graphlet handling
  • 14. Page 14 Graphlet Example „Scholarly Contribution“
  • 15. Page 15 Graphlet Example „Secutiry Advise“
  • 16. Page 16 Factual Base entities Real world Granularity Atomic Entities Evolution Addition/deletion of facts Collaboration Fact enrichment From Factual Knowledge Graphs Today
  • 17. Page 17 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
  • 19. Page 19 How did information flows change in the digital era?
  • 20. Page 20 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
  • 22. Page 22 Scholarly Communication has not changed (much) 17th century 19th century 20th century 21th century
  • 23. Page 23 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
  • 24. Page 24 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
  • 25. Page 25 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
  • 27. Page 27 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
  • 28. Page 28 Chemistry Example: CRISPR Genome Editing Source: https://cacm.acm.org/system/assets/0002/2618/021116_Google_KnowledgeGraph.large.jpg?1476779500&1455222197
  • 29. Page 29 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
  • 30. Page 30 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?
  • 31. Page 31 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?
  • 32. Page 32 The Open Research Knowledge Graph
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  • 40. To create a scholarly knowledge graph, a transformation from unstructured to structured knowledge should happen ORKG | Knowledge transformation Unstructured knowledge Structured knowledge Can we use Natural Language Processing (NLP) for the transformation process?
  • 41. ● NLP techniques are not sufficiently accurate to perform this task autonomously ● But we can intertwine machine intelligence with human intelligence to get a synergy → the best of both worlds! ORKG | Knowledge transformation Can we use Natural Language Processing (NLP) for the transformation process? 74% 84% 78% x x = 48% Error propagation
  • 42. Manual data entry Gradations of automation Human-in-the-loop Machine-in-the-loop Fully automated Human adds paper manually Human is assisted by a machine Assistance Assistance Machine is assisted by a human Machine adds paper automatically Better scalable
  • 43. Manual data entry Gradations of automation Human-in-the-loop Machine-in-the-loop Fully automated Human adds paper manually Human is assisted by a machine Assistance Assistance Machine is assisted by a human Machine adds paper automatically Better scalable Human-in-the-loop Machine-in-the-loop Human is assisted by a machine Assistance Assistance Machine is assisted by a human
  • 44. Gradations of automation Human-in-the-loop Machine-in-the-loop Human-in-the-loop Machine-in-the-loop 1. Add paper wizard 2. Paper annotator 3. TinyGenius Main entry point of adding new papers to the ORKG Annotation of key sentences in scholarly PDF articles Microtasks to validate NLP generated statements
  • 45. Gradations of automation Human-in-the-loop Machine-in-the-loop Human-in-the-loop Machine-in-the-loop 1. Add paper wizard 2. Paper annotator 3. TinyGenius Main entry point of adding new papers to the ORKG Annotation of key sentences in scholarly PDF articles Microtasks to validate NLP generated statements
  • 46. Machine-in-the-loop | Add paper wizard | Step 1 ● Collect metadata of paper ● Fetched automatically if a DOI is available ● Manual entry possible
  • 47. Machine-in-the-loop | Add paper wizard | Step 2 ● Selection of a research field ● Shows the ORKG research field taxonomy
  • 48. Machine-in-the-loop | Add paper wizard | Step 3 The third step is the description of contribution data Machine-in-the- loop
  • 49. Add paper wizard - Step 3 ● The third step is the description of contribution data ● This includes the possibility to annotate the abstract ● The user is in charge and make the final decision on whether the automatically generated data is added on not (i.e., machine-in-the-loop) ● Annotations can be added or removed ● A confidence slider hides suggestions with a low score
  • 50. Machine-in-the-loop | Add paper wizard Try it yourself! https://www.orkg.org/orkg/add-paper
  • 51. Gradations of automation Human-in-the-loop Machine-in-the-loop Human-in-the-loop Machine-in-the-loop 1. Add paper wizard 2. Paper annotator 3. TinyGenius Main entry point of adding new papers to the ORKG Annotation of key sentences in scholarly PDF articles Microtasks to validate NLP generated statements
  • 52. Machine-in-the-loop | Paper annotator ● Goal: annotate key sentences in scholarly articles with discourse classes ● Two machine-in-the-loop approaches: sentence highlighting and class recommendations
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  • 54. Sentence highlighting ● Highlights potentially interesting sentences within the article ● Can be ignored by users
  • 55. Class recommendations Recommends potentially relevant classes based on the selected sentence, called “Smart suggestions”
  • 56. Machine-in-the-loop | Add paper wizard Try it yourself! https://www.orkg.org/orkg/pdf-text-annotation
  • 57. ● The human takes the lead, machine assists where possible ● The user interface integration plays a key role ● Machine provides non-intrusive suggestions, wrong suggestions can easily be ignored ● Indicate to users that suggestions are based on AI (for example by using a dedicated color schema) Machine-in-the-loop takeaways
  • 58. Gradations of automation Human-in-the-loop Machine-in-the-loop Human-in-the-loop Machine-in-the-loop 1. Add paper wizard 2. Paper annotator 3. TinyGenius Main entry point of adding new papers to the ORKG Annotation of key sentences in scholarly PDF articles Microtasks to validate NLP generated statements
  • 59. ● Leverage existing NLP tools to process large quantities of scholarly data ● Ask any user/visitor to validate the statements using simple tasks (aka microtasks) ● Users that are normally “content consumers” can become “content creators” as microtasks lower the entrance barrier to contribute significantly Human-in-the-loop | TinyGenius
  • 60. ● Use question templates to ask relevant questions for a variety of NLP tasks Summarization (Hugging face) Entity Linking (Ambiverse NLU) Open Information Extraction (ORKG abstract annotator & ORKG title parser) Topic Modeling (CSO Classifier) Human-in-the-loop | TinyGenius | NLP tasks
  • 61. Show only validated statements by default Human-in-the-loop | TinyGenius | Prototype
  • 63. Page 63 1. Neuro Symbolic AI – combination of knowledge graphs and machine learning 2. Extend the concept of KGs (e.g. with graphlets) 3. Integration of Human and Machine Intelligence (e.g. with crowdsourcing) The grand KG challenges
  • 64. Page 64 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