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Prof. Dr. Sören Auer
Faculty of Electrical Engineering & Computer Science
Leibniz University of Hannover
TIB Technische Informationsbibliothek
Towards Knowledge Graph based
Representation, Augmentation and
Exploration of Scholarly Communications
Page 2
Zuse Z3: the
beginning of
Computing –
close to the
hardware
Foto: Konrad Zuse
Internet
Archiv/Deutsches
Museum/DFG
Page 3
Page 4
We can make things
more intuitive
Picture: The illustrated recipes
of lucy eldridge
http://thefoxisblack.com/2013/0
7/18/the-illustrated-recipes-of-
lucy-eldridge/
Computing more inuitive: procedural programming
Page 6Sören Auer 6
Computing more inuitive: OO programming
Page 8Sören Auer 8
Page 9
Computing even more inuitive: with cognitive data?!
Sören Auer 9
Page 10
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
Page 11
1. Graph based RDF data model consisting of S-P-O statements (facts)
2. Serialised as RDF Triples:
Et-Inf conf:organizes Antrittsvorlesung2019 .
Antrittsvorlesung2019 conf:starts “2019-20-07”^^xsd:date .
Antrittsvorlesung2019 conf:takesPlaceAt dbpedia:Hannover .
3. Publication under URL in Web, Intranet, Extranet
RDF & Linked Data in a Nutshell
Antritts-
vorlesung2019
dbpedia:Hannover
20.05.2019
Et-Inf
conf:organizes conf:starts
conf:takesPlaceInSubject Predicate Object
Page 12
Creating Knowledge Graphs with RDF
Linked Data
DHL
Post Tower
162.5 m
Bonn
Logistics Logistik
DHL International GmbH
??
located in
label
industry
headquarters
height
label
full name
located in
label
industry
headquarters
full nameDHL
Post Tower
162.5 m
Bonn
Logistics Logistik
DHL International GmbH
height
物流
label
Page 13
 Fabric of concept, class, property, relationships, entity desc.
 Uses a knowledge representation formalism (RDF, 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 14Source: https://cacm.acm.org/system/assets/0002/2618/021116_Google_KnowledgeGraph.large.jpg?1476779500&1455222197
Source:
https://pic2.zhimg.com/v2-
878ad2a55c440b18c889394a7
abaa5d3_1200x500.jpg
WDAqua project vision
● Answer natural language
questions
● Exploit knowledge encoded in
the Web of Data
● Provide QA services to
citizens, communities, and
industry
15
Q
A
Web of Data
Who is the director of
Clockwork Orange?
16
Who is the director of
Clockwork Orange?
17
Understand a
spoken question
Who is the director of
Clockwork Orange?
18
Understand a
spoken question
Analyse
question
Who is the director of
Clockwork Orange?
19
Understand a
spoken question
Analyse
question
Find data to
answer the
question
Who is the director of
Clockwork Orange?
20
Understand a
spoken question
Analyse
question
Find data to
answer the
question
Present the
answer
Who is the director of
Clockwork Orange?
21
Understand a
spoken question
Analyse
question
Find data to
answer the
question
Present the
answer
Data
source:
22
Which publications and
health reports are
related to Alzheimer in
Greece?
Understand a
spoken question
Analyse
question
Find data to
answer the
question
Present the
answer
23
Which publications and
health reports are
related to Alzheimer in
Greece?
Understand a
spoken question
Analyse
question
Find data to
answer the
question
Present the
answer
Data
sources
:
WDAquaQAarchitecture 24
Data management layer
Data layer
Query
decomposition
Data source
selection
Query
execution
Benchmarkin
g
Profiling
Data
qualityData generation
QA pipeline
configurator
Service
repository
Monitoring RESTful API Versioning
Message
dispatcher
Voice to text NL to SPARQLDisambiguator Rel. extraction
UIAnswer
generation
25
Who is the director of
Clockwork Orange?
Understand a
spoken question
Analyse
question
Find data to
answer the
question
Present the
answer
Demo:
http://wdaqua.eu/qa
Page 26
How did information flows
change in the digital era?
Page 27
Computer
Source: http://todde.bplaced.net/c64otto2.jpg
Page 28
Road Maps
Source: http://www.stanfords.co.uk/content/images/thumbs/013/0136835_sample_171296_LondonMiniStreetAtlas_A-Zpbk_carto.jpeg
Source: https://images-na.ssl-images-amazon.com/images/I/5135QBYNWHL.
_SX348_BO1,204,203,200_.jpg
Page 29
Phone Books
Source: http://www.stanfords.co.uk/content/images/thumbs/013/0136835_sample_
171296_LondonMiniStreetAtlas_A-Zpbk_carto.jpeg Source: https://i0.wp.com/media.boingboing.net/wp-content/uploads/2017/06/Batman.jpg?w=640&ssl=1
Page 30
How does it
work today?
Page 31https://www.amazon.de/s?k=smartphone&language=en_GB&crid=1514IC1D4IVOJ&sprefix=smartphon%2Caps%2C153&ref=nb_sb_ss_i_1_9, 04.2019
Page 32https://www.idealo.de/preisvergleich/ProductCategory/19116.html?qd=smartphone, 04.2019
Page 33https://www.google.de/maps/place/Atlanta,+Georgia,+USA/@33.756009,-84.4151149,13.5z/data=!4m5!3m4!1s0x88f5045d6993098d:0x66fede2f990b630b!8m2!3d33.7489954!4d-84.3879824, 04.2019
Page 34
 New means adapted to the new posibilities 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 play an important role
The World of Publishing &
Communication has profundely changed
Page 35
What about
Scholarly
Communication?
Page 36
One of the earliest research journals:
Philosophical Transactions of the Royal Society
Scientific publishing
in the 17th century
© CC BY Henry Oldenburg
Page 37
Scholarly communication
in 1865
Source: http://www.stsci.edu/~volk/old_spectroscopy_paper1.gif
Page 38
Publishing
in 1970s
Source: https://www.seas.upenn.edu/~zives/03f/cis550/codd.pdf
Page 39
WE HAVE
BUT
 Mainly based on PDF
 Is only partially machine-readable
 Does not preserve structure
 Does not allow embedding of semantics
 Does not facilitate interactivity / dynamicity / repurposing
 …
Scientific publishing today
Source: https://www.researchgate.net/publication/264412537_AGDISTIS_-_Graph-Based_Disambiguation_of_Named_Entities_using_Linked_Data
Page 40
Scholarly Communication has not changed (much)
17th century 19th century 20th century 21th century
Meanwhile other information intense domains were completely disrupted:
mail order catalogs, street maps, phone books, …
Page 41
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 42
Science and engineering articles by region, country: 2004 and 2014
Proliferation of scientific literature
Source: National Science Foundation: Science and Engineering Publication Output Trends: https://www.nsf.gov/statistics/2018/nsf18300/nsf18300.pdf
Page 43
1,500 scientists lift the lid on reproducibility
Monya Baker in Nature, 2016. 533 (7604): 452–454.
doi:10.1038/533452a:
 70% failed to reproduce at least one other
scientist's experiment
 50% failed to reproduce one of their
own experiments
Failure to reproduce results among disciplines
(in brackets own results)
Reproducibility Crisis
chemistry 87% (64%)
biology 77% (60%)
physics and engineering 69% (51%)
Earth sciences 64% (41%)
Source: © Stanford Medicine - Stanford University
Page 44
How can we avoid duplication if the terminology, research problems, approaches, methods,
characteristics, evaluations, … are not properly defined and identified?
How would you build an engine / building without properly defining their parts, relationships,
materials, characteristics …?
Duplication and Inefficiency
Source: https://thumbs.worthpoint.com/zoom/images2/1/0316/22/revell-
4-visible-8-engine-plastic_1_d2162f52c3fa3a6f72d2722f6c50b7b2.jpg
Source: http://xnewlook.com/cad-and-revit-3d-design.html/bill-ferguson-portfolio-computer-graphics-games-cad-related-3d-
models-cad-and-revit-design
Page 45
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
Schientists look for
the needle in the
haystack
Page 46
Search for CRISPR:
> 9.000 Results
Source: https://www.tib.eu/de/suchen/?id=198&tx_tibsearch_search%5Bquery%5D=CRISPR&tx_tibsearch_search%5Bsrt%5D=rk&tx_tibsearch_search%5Bcnt%5D=20, 04.2019
Page 47
How good is CRISPR
(wrt. precision, safety, cost)?
What specifics has genome
editing with insects?
Who has applied it to
butterflies?
Search for CRISPR:
> 163.000 Results
Source: https://scholar.google.de/scholar?hl=de&as_sdt=0%2C5&q=CRISPR&btnG=, 04.2019
Page 48
How can
we fix it?
Page 49
Realizing Vannevar Bush‘s
vision of Memex
Source: http://photos1.blogger.com/blogger/5874/1071/1600/Memex.jpg Source: http://tntindex.blogspot.com/2014/10/tabletalk-vannevar-bushs-memex.html
Page 50
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 51
Chemistry Example: CRISPR Genome Editing
Source: https://cacm.acm.org/system/assets/0002/2618/021116_Google_KnowledgeGraph.large.jpg?1476779500&1455222197
Page 52
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.896916
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/cas9isEvaluatedWith
Genome editing
https://www.wikidata.org/wiki/Q24630389
Page 53
KGs are proven to capture factual knowledge [1]
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
ScienceGRAPH approach:
Cognitive Knowledge Graphs
• Fabric of knowledge molecules – compact,
relatively simple, structured units of knowledge
• Can be incrementally enriched, annotated, interlinked …
[1] S Auer et al.: DBpedia: A nucleus for a web of open data. 6th Int. Semantic Web Conf. (ISWC) – 10-year best paper award.
cf. also knowledge graphs from: WikiData, BBC, Google, Bing, Thomson Reuters, AirBnB, BNY Mellon …
Page 54
Factual
Base entities Real world
Granularity Atomic Entities
Evolution
Addition/deletion
of facts
Collaboration Fact enrichment
From Factual Knowledge Graphs
Today
Page 55
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 ScienceGRAPH
Page 56
Research Challenge:
• Intuitive exploration leveraging the
rich semantic representations
• Answer natural language questions
Exploration and Question Answering
ScienceGRAPH Approach:
• KG-based QA component integration for dynamic and
automated composition of QA pipelines for cognitive
knowledge graphs (e.g. following [1])
• Round-trip refinement and integration of search,
faceted exploration, question answering and
conversational interfaces
Question
parsing
Named
Entity
Recognition
(NER) &
Linking
(NEL)
Relation
extraction
Query
con-
struction
Query
execution
Result
rendering
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 57
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 58
Page 59
Page 60
Page 61
Page 62
Page 63
Page 64
Facilitating Comparisons of Research Contributions
Page 65
High-level Data Model: RDF + Metadata
Statement
Predicate
resource_id: R1
date: 2019-01-23
user: 1234
Resource Resource
Literal
resource_id: R5
date: 2019-01-23
user: 6789
literal_id: L17
value: „ORKG“
date: 2019-01-23
user: 1234
predicate_id: P4
date: 2019-01-23
user: 6789
statement_id: S2
date: 2019-01-23
user: 6789
Page 66
Business Logic
(Data input/output, consistency)
REST API
(Interface to the outside world)
SPARQL
(Data Query
Language)
GraphQL
(API Query
Language)
Neo4j
(Linked Property Graph)
Virtuoso
(Triple Store)
Domain Model
(Statements, Resouces, etc.)
AuthN & AuthZ
(ORCID or other SSO)
Contribute Curate Explore
Third-party
Apps
?
(Other Database)
User
Interface
PersistenceDomainApplication
High-Level Architecture: Neo4j Graph Application
Term of the Gene Ontology, namely GO:0030350
The authorsThe research contribution
The research result
The paperA continuous
variable value
Page 88
More projects
Stay tuned
 https://tib.eu
 Mailinglist/group:
https://groups.google.com/forum/#!forum/orkg
 Open Research Knowledge Graph:
https://orkg.org
 ERC Consolidator Grant ScienceGRAPH
started in May
 Transfer event on International Data Space on
June 19:
https://events.tib.eu/transfer/
Page 89
The Team
Prof. (Univ. S. Bolivar)
Dr. Maria Esther Vidal
Software Development
Kemele Endris Farah Karim
Collaborators TIB/L3S Scientific Data Management
Group Leaders PostDocs
Project Management
Doctoral Researchers
Dr. Markus Stocker Dr. Gábor Kismihók Dr. Javad Chamanara Dr. Jennifer D’Souza
Olga Lezhnina Allard Oelen Yaser Jaradeh Shereif Eid
Manuel Prinz
Alex Garatzogianni Laura Granzow
Collaborators InfAI Leipzig / AKSW
Dr. Michael Martin Natanael Arndt
Sarven Capadisli Vitalis Wiens
Wazed Ali
Contact
Prof. Dr. Sören Auer
TIB & Leibniz University of Hannover
auer@tib.eu
https://de.linkedin.com/in/soerenauer
https://twitter.com/soerenauer
https://www.xing.com/profile/Soeren_Auer
http://www.researchgate.net/profile/Soeren_Auer

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Towards Knowledge Graph based Representation, Augmentation and Exploration of Scholarly Communications

  • 1. Prof. Dr. Sören Auer Faculty of Electrical Engineering & Computer Science Leibniz University of Hannover TIB Technische Informationsbibliothek Towards Knowledge Graph based Representation, Augmentation and Exploration of Scholarly Communications
  • 2. Page 2 Zuse Z3: the beginning of Computing – close to the hardware Foto: Konrad Zuse Internet Archiv/Deutsches Museum/DFG
  • 4. Page 4 We can make things more intuitive Picture: The illustrated recipes of lucy eldridge http://thefoxisblack.com/2013/0 7/18/the-illustrated-recipes-of- lucy-eldridge/
  • 5. Computing more inuitive: procedural programming
  • 7. Computing more inuitive: OO programming
  • 9. Page 9 Computing even more inuitive: with cognitive data?! Sören Auer 9
  • 10. Page 10 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
  • 11. Page 11 1. Graph based RDF data model consisting of S-P-O statements (facts) 2. Serialised as RDF Triples: Et-Inf conf:organizes Antrittsvorlesung2019 . Antrittsvorlesung2019 conf:starts “2019-20-07”^^xsd:date . Antrittsvorlesung2019 conf:takesPlaceAt dbpedia:Hannover . 3. Publication under URL in Web, Intranet, Extranet RDF & Linked Data in a Nutshell Antritts- vorlesung2019 dbpedia:Hannover 20.05.2019 Et-Inf conf:organizes conf:starts conf:takesPlaceInSubject Predicate Object
  • 12. Page 12 Creating Knowledge Graphs with RDF Linked Data DHL Post Tower 162.5 m Bonn Logistics Logistik DHL International GmbH ?? located in label industry headquarters height label full name located in label industry headquarters full nameDHL Post Tower 162.5 m Bonn Logistics Logistik DHL International GmbH height 物流 label
  • 13. Page 13  Fabric of concept, class, property, relationships, entity desc.  Uses a knowledge representation formalism (RDF, 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
  • 15. WDAqua project vision ● Answer natural language questions ● Exploit knowledge encoded in the Web of Data ● Provide QA services to citizens, communities, and industry 15 Q A Web of Data
  • 16. Who is the director of Clockwork Orange? 16
  • 17. Who is the director of Clockwork Orange? 17 Understand a spoken question
  • 18. Who is the director of Clockwork Orange? 18 Understand a spoken question Analyse question
  • 19. Who is the director of Clockwork Orange? 19 Understand a spoken question Analyse question Find data to answer the question
  • 20. Who is the director of Clockwork Orange? 20 Understand a spoken question Analyse question Find data to answer the question Present the answer
  • 21. Who is the director of Clockwork Orange? 21 Understand a spoken question Analyse question Find data to answer the question Present the answer Data source:
  • 22. 22 Which publications and health reports are related to Alzheimer in Greece? Understand a spoken question Analyse question Find data to answer the question Present the answer
  • 23. 23 Which publications and health reports are related to Alzheimer in Greece? Understand a spoken question Analyse question Find data to answer the question Present the answer Data sources :
  • 24. WDAquaQAarchitecture 24 Data management layer Data layer Query decomposition Data source selection Query execution Benchmarkin g Profiling Data qualityData generation QA pipeline configurator Service repository Monitoring RESTful API Versioning Message dispatcher Voice to text NL to SPARQLDisambiguator Rel. extraction UIAnswer generation
  • 25. 25 Who is the director of Clockwork Orange? Understand a spoken question Analyse question Find data to answer the question Present the answer Demo: http://wdaqua.eu/qa
  • 26. Page 26 How did information flows change in the digital era?
  • 28. Page 28 Road Maps Source: http://www.stanfords.co.uk/content/images/thumbs/013/0136835_sample_171296_LondonMiniStreetAtlas_A-Zpbk_carto.jpeg Source: https://images-na.ssl-images-amazon.com/images/I/5135QBYNWHL. _SX348_BO1,204,203,200_.jpg
  • 29. Page 29 Phone Books Source: http://www.stanfords.co.uk/content/images/thumbs/013/0136835_sample_ 171296_LondonMiniStreetAtlas_A-Zpbk_carto.jpeg Source: https://i0.wp.com/media.boingboing.net/wp-content/uploads/2017/06/Batman.jpg?w=640&ssl=1
  • 30. Page 30 How does it work today?
  • 34. Page 34  New means adapted to the new posibilities 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 play an important role The World of Publishing & Communication has profundely changed
  • 36. Page 36 One of the earliest research journals: Philosophical Transactions of the Royal Society Scientific publishing in the 17th century © CC BY Henry Oldenburg
  • 37. Page 37 Scholarly communication in 1865 Source: http://www.stsci.edu/~volk/old_spectroscopy_paper1.gif
  • 38. Page 38 Publishing in 1970s Source: https://www.seas.upenn.edu/~zives/03f/cis550/codd.pdf
  • 39. Page 39 WE HAVE BUT  Mainly based on PDF  Is only partially machine-readable  Does not preserve structure  Does not allow embedding of semantics  Does not facilitate interactivity / dynamicity / repurposing  … Scientific publishing today Source: https://www.researchgate.net/publication/264412537_AGDISTIS_-_Graph-Based_Disambiguation_of_Named_Entities_using_Linked_Data
  • 40. Page 40 Scholarly Communication has not changed (much) 17th century 19th century 20th century 21th century Meanwhile other information intense domains were completely disrupted: mail order catalogs, street maps, phone books, …
  • 41. Page 41 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
  • 42. Page 42 Science and engineering articles by region, country: 2004 and 2014 Proliferation of scientific literature Source: National Science Foundation: Science and Engineering Publication Output Trends: https://www.nsf.gov/statistics/2018/nsf18300/nsf18300.pdf
  • 43. Page 43 1,500 scientists lift the lid on reproducibility Monya Baker in Nature, 2016. 533 (7604): 452–454. doi:10.1038/533452a:  70% failed to reproduce at least one other scientist's experiment  50% failed to reproduce one of their own experiments Failure to reproduce results among disciplines (in brackets own results) Reproducibility Crisis chemistry 87% (64%) biology 77% (60%) physics and engineering 69% (51%) Earth sciences 64% (41%) Source: © Stanford Medicine - Stanford University
  • 44. Page 44 How can we avoid duplication if the terminology, research problems, approaches, methods, characteristics, evaluations, … are not properly defined and identified? How would you build an engine / building without properly defining their parts, relationships, materials, characteristics …? Duplication and Inefficiency Source: https://thumbs.worthpoint.com/zoom/images2/1/0316/22/revell- 4-visible-8-engine-plastic_1_d2162f52c3fa3a6f72d2722f6c50b7b2.jpg Source: http://xnewlook.com/cad-and-revit-3d-design.html/bill-ferguson-portfolio-computer-graphics-games-cad-related-3d- models-cad-and-revit-design
  • 45. Page 45 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 Schientists look for the needle in the haystack
  • 46. Page 46 Search for CRISPR: > 9.000 Results Source: https://www.tib.eu/de/suchen/?id=198&tx_tibsearch_search%5Bquery%5D=CRISPR&tx_tibsearch_search%5Bsrt%5D=rk&tx_tibsearch_search%5Bcnt%5D=20, 04.2019
  • 47. Page 47 How good is CRISPR (wrt. precision, safety, cost)? What specifics has genome editing with insects? Who has applied it to butterflies? Search for CRISPR: > 163.000 Results Source: https://scholar.google.de/scholar?hl=de&as_sdt=0%2C5&q=CRISPR&btnG=, 04.2019
  • 49. Page 49 Realizing Vannevar Bush‘s vision of Memex Source: http://photos1.blogger.com/blogger/5874/1071/1600/Memex.jpg Source: http://tntindex.blogspot.com/2014/10/tabletalk-vannevar-bushs-memex.html
  • 50. Page 50 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
  • 51. Page 51 Chemistry Example: CRISPR Genome Editing Source: https://cacm.acm.org/system/assets/0002/2618/021116_Google_KnowledgeGraph.large.jpg?1476779500&1455222197
  • 52. Page 52 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.896916 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/cas9isEvaluatedWith Genome editing https://www.wikidata.org/wiki/Q24630389
  • 53. Page 53 KGs are proven to capture factual knowledge [1] 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 ScienceGRAPH approach: Cognitive Knowledge Graphs • Fabric of knowledge molecules – compact, relatively simple, structured units of knowledge • Can be incrementally enriched, annotated, interlinked … [1] S Auer et al.: DBpedia: A nucleus for a web of open data. 6th Int. Semantic Web Conf. (ISWC) – 10-year best paper award. cf. also knowledge graphs from: WikiData, BBC, Google, Bing, Thomson Reuters, AirBnB, BNY Mellon …
  • 54. Page 54 Factual Base entities Real world Granularity Atomic Entities Evolution Addition/deletion of facts Collaboration Fact enrichment From Factual Knowledge Graphs Today
  • 55. Page 55 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 ScienceGRAPH
  • 56. Page 56 Research Challenge: • Intuitive exploration leveraging the rich semantic representations • Answer natural language questions Exploration and Question Answering ScienceGRAPH Approach: • KG-based QA component integration for dynamic and automated composition of QA pipelines for cognitive knowledge graphs (e.g. following [1]) • Round-trip refinement and integration of search, faceted exploration, question answering and conversational interfaces Question parsing Named Entity Recognition (NER) & Linking (NEL) Relation extraction Query con- struction Query execution Result rendering 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?
  • 57. Page 57 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?
  • 64. Page 64 Facilitating Comparisons of Research Contributions
  • 65. Page 65 High-level Data Model: RDF + Metadata Statement Predicate resource_id: R1 date: 2019-01-23 user: 1234 Resource Resource Literal resource_id: R5 date: 2019-01-23 user: 6789 literal_id: L17 value: „ORKG“ date: 2019-01-23 user: 1234 predicate_id: P4 date: 2019-01-23 user: 6789 statement_id: S2 date: 2019-01-23 user: 6789
  • 66. Page 66 Business Logic (Data input/output, consistency) REST API (Interface to the outside world) SPARQL (Data Query Language) GraphQL (API Query Language) Neo4j (Linked Property Graph) Virtuoso (Triple Store) Domain Model (Statements, Resouces, etc.) AuthN & AuthZ (ORCID or other SSO) Contribute Curate Explore Third-party Apps ? (Other Database) User Interface PersistenceDomainApplication High-Level Architecture: Neo4j Graph Application
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  • 83. Term of the Gene Ontology, namely GO:0030350
  • 84.
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  • 87. The authorsThe research contribution The research result The paperA continuous variable value
  • 88. Page 88 More projects Stay tuned  https://tib.eu  Mailinglist/group: https://groups.google.com/forum/#!forum/orkg  Open Research Knowledge Graph: https://orkg.org  ERC Consolidator Grant ScienceGRAPH started in May  Transfer event on International Data Space on June 19: https://events.tib.eu/transfer/
  • 89. Page 89 The Team Prof. (Univ. S. Bolivar) Dr. Maria Esther Vidal Software Development Kemele Endris Farah Karim Collaborators TIB/L3S Scientific Data Management Group Leaders PostDocs Project Management Doctoral Researchers Dr. Markus Stocker Dr. Gábor Kismihók Dr. Javad Chamanara Dr. Jennifer D’Souza Olga Lezhnina Allard Oelen Yaser Jaradeh Shereif Eid Manuel Prinz Alex Garatzogianni Laura Granzow Collaborators InfAI Leipzig / AKSW Dr. Michael Martin Natanael Arndt Sarven Capadisli Vitalis Wiens Wazed Ali
  • 90. Contact Prof. Dr. Sören Auer TIB & Leibniz University of Hannover auer@tib.eu https://de.linkedin.com/in/soerenauer https://twitter.com/soerenauer https://www.xing.com/profile/Soeren_Auer http://www.researchgate.net/profile/Soeren_Auer

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. 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
  4. 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
  5. We reproduce a statistical hypothesis test published as a result in this paper, namely https://doi.org/10.1093/eurheartj/ehw333 We represent this result in a machine readable form following the concept description for a kind of statistical hypothesis test of the statistical methods ontology (STATO), namely http://purl.obolibrary.org/obo/STATO_0000304 We store the representation in the ORKG database
  6. Specifically, we replicate, describe in machine readable form, and store in the ORKG database the statistical hypothesis test result highlighted and shown here in human readable form Note that the relevant information is presented in multiple modalities, both text and images, and none of them is easily read and interpreted by machines In particular, the relevant data is presented as plot in Figure 1 B (image) Furthermore, the kind of statistical hypothesis test performed, the fact that IRE binding activity is the dependent variable, and the p-value are all implicit information.
  7. We conduct the statistical hypothesis test in Jupyter using Python We have IRE binding activity data for two groups, called non-failing heart (i.e., healthy individuals) and failing heart (i.e., patients) We compute a t-test and obtain a p-value This is the classical workflow a typical researcher would do using SPSS or similar statistical computing environment However, in contrast to the classical workflow, here we represent and store a machine readable description of the statistical hypothesis test (one that includes the input data, the output p-value, the dependent variable, and the kind of statistical hypothesis test used) in the ORKG Later, when the paper and its results are published we will be able to relate to this result in the overall research contribution description Let’s look at how this is done using the ORKG User Interface
  8. We add a paper by DOI lookup or alternatively manually (e.g., if a DOI is not available)
  9. The bibliographic metadata about the paper (title, authors, etc.) is automatically fetched from Crossref and displayed in the user interface
  10. Users can then classify the paper according to research field
  11. More interesting is the possibility to describe the research contributions this paper makes First, researchers and provide a research problem description
  12. Next, the researcher can further describe the contribution Here we show how to link to the statistical hypothesis test result obtained earlier in data analysis and published in the paper as a result of this research contribution We say that research contributions “yield” research results; hence, the “Yields” attribute shown here The machine readable result has a human readable label which is shown in the user interface by simply typing some included words, here “IRE” The user can select the correct result and save it
  13. The research contribution can be further described, e.g. the approach used The paper may make further contributions, which can be described as well For the purpose here, we skip this and move to the next step
  14. That’s it, the paper description, its research contribution, addressed problem and one result are added Note that we did not describe the research result, the statistical hypothesis test conducted earlier. We just linked to it! Let’s look at the paper
  15. The paper can be browsed by research field and is shown as recently added It can be selected here
  16. Here we see the details, in addition to bibliographic metadata the research contributions of this paper For the research contribution we just described, we see the problem and we can now inspect the yielded research result
  17. In addition to a human readable label, the statistical hypothesis test description has a specific type, has three inputs and an output, namely the p-value Let’s look at the output
  18. The output is indeed typed as a p-value, a concept of the Ontology for Biomedical Investigations (i.e., http://purl.obolibrary.org/obo/OBI_0000175) It has a value specification, namely the specific value computed earlier in data analysis We can take a look at the value by expanding the value specification
  19. Here it is, the specified numeric value of the computed p-value typed as a scalar value specification, another term of the Ontology for Biomedical Investigations (i.e., http://purl.obolibrary.org/obo/OBI_0001931) Now, let’s go back to our research result description and take a look at the three specified inputs of the statistical hypothesis test
  20. Here they are: The study design dependent variable and the two continuous variables for failing and non-failing hearts Let’s take a quick look at what was the study design dependent variable
  21. Where, it was Iron-Responsive Element (IRE) binding Which is a term of the Gene Ontology (i.e., http://purl.obolibrary.org/obo/GO_0030350)
  22. Finally, let’s take a look at the non-failing heart continuous variable used as specified input in the statistical hypothesis test Each continuous variable (a term of the statistical methods ontology) has parts, namely scalar measurement data These are the actual data values and we can explore them
  23. Here is an example, the numeric value 105.0 of the value specification #4 in the non-failing heart continuous variable
  24. Here is an example, the numeric value 105.0 of the value specification #4 in the non-failing heart continuous variable
  25. And of course it is all a knowledge graph