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Prof. Dr. Sören Auer
Leibniz University of Hannover
TIB Technische Informationsbibliothek
Describing Scholarly Contributions
semantically with the
Open Research Knowledge Graph
Page 2
How did information flows change
in the digital era?
Page 3
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 4
What about
Scholarly
Communication?
Page 5
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 6
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 7
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 8
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 9
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 10
How can
we fix it?
Page 11
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 12
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 13
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
Towards 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 14
Factual
Base entities Real world
Granularity Atomic Entities
Evolution
Addition/deletion
of facts
Collaboration Fact enrichment
From Factual Knowledge Graphs
Today
Page 15
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 16
Chemistry Example: CRISPR Genome Editing
Source: https://cacm.acm.org/system/assets/0002/2618/021116_Google_KnowledgeGraph.large.jpg?1476779500&1455222197
Page 17
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 18
Research Challenge:
• Intuitive exploration leveraging the
rich semantic representations
• Answer natural language questions
Exploration and Question Answering
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 19
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?
Demo: Open Research
Knowledge Graph
Describing Scholarly Contributions semantically with the Open Research Knowledge Graph
Describing Scholarly Contributions semantically with the Open Research Knowledge Graph
Describing Scholarly Contributions semantically with the Open Research Knowledge Graph
Describing Scholarly Contributions semantically with the Open Research Knowledge Graph
Describing Scholarly Contributions semantically with the Open Research Knowledge Graph
Describing Scholarly Contributions semantically with the Open Research Knowledge Graph
Recent ORKG Developments
Page 28
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
Page 29
Domain specific
Research
Contribution
Templates
Page 30
Page 31
Survey
Table
Extraction
Page 32
Page 33
Page 34
Publishing
State-of-the-Art
Comparisons
Page 35
Organize
Research
Contributions
along research
problems
Page 36
Highlighting
Contributors
and Authors
Page 37
Domain Observatories
Make organizations supporting the
curation of research in a certain
domain more visible
Page 38
Showcase
contributing
organizations
Page 39
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 FarfarMuhammad Haris
Administration
Katja Bartel Simone Matern
Page 40
Conclusions
Stay tuned
 https://tib.eu
 Consider creating an ORKG observatory for
your domain
 Mailinglist/group:
https://groups.google.com/forum/#!forum/orkg
 Open Research Knowledge Graph:
https://orkg.org
 ERC Consolidator Grant ScienceGRAPH
on the topic
 We need to reinvent scholarly
communication
 Knowledge Graphs are perfectly suited to
capture research contributions in a
structured and semantic way making them
human and machine interpretable
 With our Open Research Knowledge Graph
initiative we aim to establish a registry for
research contributions
 Curation and synergistic combination of
human, expert and machine intelligence is a
challenge
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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Describing Scholarly Contributions semantically with the Open Research Knowledge Graph

  • 1. Prof. Dr. Sören Auer Leibniz University of Hannover TIB Technische Informationsbibliothek Describing Scholarly Contributions semantically with the Open Research Knowledge Graph
  • 2. Page 2 How did information flows change in the digital era?
  • 3. Page 3 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
  • 5. Page 5 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, …
  • 6. Page 6 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
  • 7. Page 7 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
  • 8. Page 8 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
  • 9. Page 9 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
  • 11. Page 11 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
  • 12. Page 12 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
  • 13. Page 13 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 Towards 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 …
  • 14. Page 14 Factual Base entities Real world Granularity Atomic Entities Evolution Addition/deletion of facts Collaboration Fact enrichment From Factual Knowledge Graphs Today
  • 15. Page 15 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
  • 16. Page 16 Chemistry Example: CRISPR Genome Editing Source: https://cacm.acm.org/system/assets/0002/2618/021116_Google_KnowledgeGraph.large.jpg?1476779500&1455222197
  • 17. Page 17 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
  • 18. Page 18 Research Challenge: • Intuitive exploration leveraging the rich semantic representations • Answer natural language questions Exploration and Question Answering 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?
  • 19. Page 19 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?
  • 28. Page 28 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
  • 37. Page 37 Domain Observatories Make organizations supporting the curation of research in a certain domain more visible
  • 39. Page 39 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 FarfarMuhammad Haris Administration Katja Bartel Simone Matern
  • 40. Page 40 Conclusions Stay tuned  https://tib.eu  Consider creating an ORKG observatory for your domain  Mailinglist/group: https://groups.google.com/forum/#!forum/orkg  Open Research Knowledge Graph: https://orkg.org  ERC Consolidator Grant ScienceGRAPH on the topic  We need to reinvent scholarly communication  Knowledge Graphs are perfectly suited to capture research contributions in a structured and semantic way making them human and machine interpretable  With our Open Research Knowledge Graph initiative we aim to establish a registry for research contributions  Curation and synergistic combination of human, expert and machine intelligence is a challenge
  • 41. 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. 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
  2. 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