The document-oriented workflows in science have reached (or already exceeded) the limits of adequacy as highlighted for example by recent discussions on the increasing proliferation of scientific literature and the reproducibility crisis. Now it is possible to rethink this dominant paradigm of document-centered knowledge exchange and transform it into knowledge-based information flows by representing and expressing knowledge through semantically rich, interlinked knowledge graphs. The core of the establishment of knowledge-based information flows is the creation and evolution of information models for the establishment of a common understanding of data and information between the various stakeholders as well as the integration of these technologies into the infrastructure and processes of search and knowledge exchange in the research library of the future. By integrating these information models into existing and new research infrastructure services, the information structures that are currently still implicit and deeply hidden in documents can be made explicit and directly usable. This has the potential to revolutionize scientific work because information and research results can be seamlessly interlinked with each other and better mapped to complex information needs. Also research results become directly comparable and easier to reuse.

1. Towards an
Open Research Knowledge
Graph
Sören Auer

3. Gottfried Wilhelm Leibniz
* 21. Juni/ 1. Juli 1646 in Leipzig
† 14. November 1716 in Hannover
Namesake Member of
Library of
Namesake

4. Had to do some research on
serials…

5. 5
Serials
Mail order catalogs

6. 6

7. 7
Mail order catalogs

8. 8

9. 9

10. 10
Road Maps

11. 11
Phone Books

12. How does it work today?

13. 13

14. 14

15. 15

16. 16
New means adapted to the new posibilities were developed, e.g. „zooming“,
dynamics
Business models changed completely
More focus on data, interlinking of data and services and search in the data
Integration, crowdsourcing play an important role
The World of Publishing & Communication
has profundely changed

17. What about Scholarly
Communication?

18. 18
Scientific publishing in the 17th
century
One of the earliest research
journals: Philosophical Transactions of the
Royal Society
© CC BY Henry Oldenburg

19. 19
Publishing in 1970s

20. 20
Scientific publishing today
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
• …

21. 21
Proliferation of scientific literature
Duplication and inefficiency
Deficiency of peer-review
Reproducibility crisis
Science is Seriously Flawed

22. 22
Science and engineering articles by region, country: 2004 and 2014
Proliferation of scientific literature
National Science Foundation: Science and Engineering Publication Output Trends: https://www.nsf.gov/statistics/2018/nsf18300/nsf18300.pdf

23. 23
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):
• chemistry: 87% (64%),
• biology: 77% (60%),
• physics and engineering: 69% (51%),
• Earth sciences: 64% (41%).
Reproducibility Crisis
© Stanford Medicine - Stanford University

24. 24
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

25. 25
Lack of:
• 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
Root Cause - Deficiency of Scholarly
Communication?

26. How can we fix it?
26

27. 27
Realizing Vannevar Bush‘s
vision of Memex

28. 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
28
[1] Auer, Lehmann, Ngomo, Zaveri: Introduction to Linked Data and Its Lifecycle on the Web. Reasoning Web 2013

29. Page 29
1. Graph based RDF data model consisting of S-P-O statements (facts)
RDF & Linked Data in a Nutshell
NasigConf2018
dbpedia:Atlanta
09.06.2018
NASIG
conf:organizes
conf:starts
conf:takesPlaceIn
2. Serialised as RDF Triples:
NASIG conf:organizes NasigConf2018 .
NasigConf2018 conf:starts “2018-06-09”^^xsd:date .
NasigConf2018 conf:takesPlaceAt dbpedia:Atlanta .
3. Publication under URL in Web, Intranet, Extranet
Subject Predicate Object

30. Page 30
Creating Knowledge Graphs with RDF
Linked Data
located in
label
industry
headquarters
full nameDHL
Post Tower
162.5 m
Bonn
Logistics Logistik
DHL International GmbH
height
物流
label

31. Page 31
Graph consists of:
 Resources (identified via URIs)
 Literals: data values with data type (URI) or language (multilinguality integrated)
 Attributes of resources are also URI-identified (from vocabularies)
Various data sources and vocabularies can be arbitrarily mixed and meshed
URIs can be shortened with namespace prefixes; e.g. dbp: → http://dbpedia.org/resource/
RDF Data Model (a bit more technical)
gn:locatedIn
rdfs:label
dbo:industry
ex:headquarters
foaf:namedbp:DHL_International_GmbH
dbp:Post_Tower
"162.5"^^xsd:decimal
dbp:Bonn
dbp:Logistics
"Logistik"@de
"DHL International GmbH"^^xsd:string
ex:height
"物流"@zh
rdfs:label
rdf:value
unit:Meter
ex:unit

32. Page 32
• 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

33. Page 33

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. Paradigm Change in Scholarly Communication
Towards more Knowledge-based Information Flows

36. 36
Paradigm Change in Scholarly Communication Knowledge-based
Information Flows in Science & Technology
Challenges: Digitalisation of Science, monopolisation by commercial actors,
Proliferation of publications, Reproducibility Crisis

37. 37
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
• …
Open Research Knowledge Graph
Overarching Concepts
 Research problems
 Definitions
 Research approaches
 Methods
Artefacts
 Publications
 Data
 Software
 Image/Audio/Video
 Knowledge Graphs / Ontologies
Domain specific concepts
Open Research Knowledge
Graph makes comprehensive
and subject-specific concepts
clearly identifiable and links
them semantically (with
clearly described relations)
with each other and with
relevant further artifacts.

38. 38

39. 39
Search for CRISPR:
>4.000 Results

40. 40
Chemistry Example: CRISPR/Cas Genome Editing

41. 41
Semantic Representation using a Knowledge Graph
Author Robert Reed
Research Problem
Methods
Experimental Data
related Concepts
Genome editing in Lepidoptera
CRISPR/cas9
Lepidoptera; Genome editing; CRSIPR
https://doi.org/10.5281/zenodo.896916
A practial guide to 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/10.528
1/zenodo.896916
isAuthorOf
adresses
CRSPRS/cas9
isImplementedBy
isEvaluatedWith
Genome editing
<https://www.wikidata.or
g/wiki/Q24630389>
relatesConcept
3. Graph representation
2. Graph Curation Form
1. Original Publication

42. 42
Automatic Generation of Comparisons/Surveys

43. 43
Open Research Knowledge Graph
interlinks existing Services and Resources

44. 44

45. Interlinking Article, Software, Video and
Graph resources describing the research

47. 47
Advantages of knowledge based scholarly communication
 Clear identification of all relevant artifacts, concepts, attributes, relationships 
terminological and conceptual precision and sharpness, less ambiguity
 Better and explicit networking of all relevant artifacts and information sources 
traceability
 ORKG machine-readability  new search, retrieval, mining and assistance
applications
 Avoidance of media discontinuities in the different phases of scientific work 
Increased efficiency
 Use of concepts and relationships across disciplinary boundaries 
Interdisciplinarity and transdisciplinarity
 Halting the proliferation of scientific publications  less duplication
 Facilitating the entry of young academics or laypersons  Open Science

48. 48
There is a lot to do:
• Equip existing services with Linked Data interfaces
• Enable the deep semantic description of research, requires
• Good user interfaces
• Scalable storage and search facility
• Collaboration between scientists, libariens, knowledge engineers, machines
Stay tuned
• Mailinglist/group: https://groups.google.com/forum/#!forum/orkg
• Comming soon: Open Research Knowledge Graph: https://orkg.org
• Next workshop at TIB on November, 22nd (after DILS Conference:
https://events.tib.eu/dils2018/)
Outlook

49. 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
Soeren.Auer@tib.eu
Sören Auer

50. 50
Said Fathalla, Sahar Vahdati, Sören Auer, Christoph Lange:
Towards a Knowledge Graph Representing Research Findings by Semantifying
Survey Articles. TPDL 2017: 315-327,
https://www.researchgate.net/publication/319419350
Sahar Vahdati, Natanael Arndt, Sören Auer, Christoph Lange:
OpenResearch: Collaborative Management of Scholarly Communication Metadata.
EKAW 2016: 778-793, https://www.researchgate.net/publication/309700661
Sören Auer: Towards an Open Research Knowledge Graph
https://zenodo.org/record/1157185
Sören Auer, Viktor Kovtun, Manuel Prinz, Anna Kasprzik, Markus Stocker: Towards a
Knowledge Graph for Science. https://doi.org/10.15488/3401
References