1.
THE CHALLENGE OF
DEEPER KNOWLEDGE
GRAPHS FOR SCIENCEPAUL GROTH | @PGROTH | PGROTH.COM
CONTRIBUTIONS: RON DANIEL, MICHAEL LAURUHN & @ELSEVIERLABS TEAM

2.
OUTLINE
▸Research Performance
▸Knowledge Graphs
▸Research as a low resource domain
▸Quality

3.
Bloom, N., Jones, C. I., Van Reenen, J., & Webb,
M. (2017). Are ideas getting harder to find? (No.
w23782). National Bureau of Economic
Research.
Slides: https://web.stanford.edu/~chadj/slides-
ideas.pdf

4.
Bloom, N., Jones, C. I., Van Reenen, J., & Webb,
M. (2017). Are ideas getting harder to find? (No.
w23782). National Bureau of Economic
Research.
Slides: https://web.stanford.edu/~chadj/slides-
ideas.pdf

5.
Bloom, N., Jones, C. I., Van Reenen, J., & Webb,
M. (2017). Are ideas getting harder to find? (No.
w23782). National Bureau of Economic
Research.
Slides: https://web.stanford.edu/~chadj/slides-
ideas.pdf

6.
Bloom, N., Jones, C. I., Van Reenen, J., & Webb,
M. (2017). Are ideas getting harder to find? (No.
w23782). National Bureau of Economic
Research.
Slides: https://web.stanford.edu/~chadj/slides-
ideas.pdf

7.
Bloom, N., Jones, C. I., Van Reenen, J., & Webb,
M. (2017). Are ideas getting harder to find? (No.
w23782). National Bureau of Economic
Research.
Slides: https://web.stanford.edu/~chadj/slides-
ideas.pdf

8.
WHY?
INFORMATION OVERLOAD

9.
WHY?
IN PRACTICE
Gregory, K., Groth, P., Cousijn, H., Scharnhorst, A., & Wyatt, S. (2017).
Searching Data: A Review of Observational Data Retrieval Practices.
arXiv preprint arXiv:1707.06937.
Some observations from @gregory_km
survey & interviews :
• The needs and behaviors of specific user groups (e.g.
early career researchers, policy makers, students) are
not well documented.
• Participants require details about data collection and
handling
• Reconstructing data tables from journal articles,
using general search engines, and making direct
data requests are common.
K Gregory, H Cousijn, P Groth, A Scharnhorst, S Wyatt (2018).
Understanding Data Retrieval Practices: A Social Informatics Perspective.
arXiv preprint arXiv:1801.04971

10.
THE ROLE OF METADATA IN THE SECOND MACHINE AGE – DC-2016 / KØBENHAVN / 13 OCTOBER
ANSWERS ARE ABOUT THINGS, NOT JUST WORKS
Why shouldn’t a search on an author return information
about the author, including the author’s works? Where
was the author born, when did she live, what is she
known for? … All of this is possible, but only if we can
make some fundamental changes in our approach to
bibliographic description. ... The challenge for us lies in
transforming what we can of our data into
interrelated “things” without overindulging that
metaphor.
Coyle, K. (2016). FRBR, before and after: a look at our bibliographical
models. Chicago: ALA Editions.

11.
ENTER
KNOWLEDGE
GRAPHS
ERNST, PATRICK, ET AL. "DEEPLIFE: AN ENTITY-
AWARE SEARCH, ANALYTICS AND EXPLORATION
PLATFORM FOR HEALTH AND LIFE SCIENCES."
PROCEEDINGS OF ACL-2016 SYSTEM
DEMONSTRATIONS (2016): 19-24.

12.
Knowledge Graphs: The
Science System

13.
Knowledge Graphs:
Curated Databases
From: Wikidata as a semantic framework for the Gene Wiki initiative
Database (Oxford). 2016;2016. doi:10.1093/database/baw015

14.
RESEARCH IS
DIVERSE
http://knowescape.org/map-of-science-an-update/

15.
15
Augenstein, Isabelle, et al. "SemEval 2017 Task 10:
ScienceIE-Extracting Keyphrases and Relations from
Scientific Publications." Proceedings of the 11th
International Workshop on Semantic Evaluation
(SemEval-2017). 2017.
SCIENTIFIC TEXT IS CHALLENGING

16.
UNSUPERVISED & DISTANT SUPERVISION
EXAMPLE: UNIVERSAL SCHEMAS AND REVERB
Groth et al., Applying Universal Schemas for Domain Specific Ontology Expansion http://www.akbc.ws/2016/papers/3_Paper.pdf
• Successful in predicting new triples
(F1 =~ .7)
• ReVerb’s relations very interesting,
but recall very low
• Was not domain independent
• Matched arguments against a
medical ontology to improve
precision
• Predicted relations were restricted
to relation types from the same
ontology

17.
OPEN INFORMATION EXTRACTION IN SCIENCE IS
HARD
Open Information Extraction on Scientific Text: An Evaluation.
Paul Groth, Mike Lauruhn, Antony Scerri and Ron Daniel, Jr.. COLING
2018
Example:
“The patient was treated with Emtricitabine,
Etravirine, and Darunavir”
‣ (The patient :: was treated with :: Emtricitabine,
Etravirine, and Darunavir)
Another possible extraction is:
‣ (The patient :: was treated with :: Emtricitabine)
‣ (The patient :: was treated with :: Etravirine)
‣ (The patient :: was treated with :: Darunavir)
698 unique relation types – 400 relation types

18.
CROWDS ARE NOT EXPERTS
Use of Internal Testing Data to Help Determine Compensation for
Crowdsourcing Tasks
Michael Lauruhn, Paul Groth, Corey Harper, Helena Deus. HUML 2018

19.
TRANSFER LEARNING
Sujit Pal @ Elsevier Labs

20.
TRANSFER LEARNING & MACHINE DEPENDENCIES

21.
QUALITY IS
DEPENDENT
ON SOURCES

22.
PROVENANCE

23.
SOURCES AREN’T JUST DATA
Lauruhn, Michael, and Paul Groth. "Sources of
Change for Modern Knowledge Organization
Systems." Knowledge Organization 43, no. 8
(2016).

24.
A MORE TRANSPARENT SUPPLY CHAIN
Groth, Paul, "Transparency and Reliability in the Data Supply
Chain," Internet Computing, IEEE, vol.17, no.2, pp.69,71, March-
April 2013 doi: 10.1109/MIC.2013.41

25.
1) https://www.elsevier.com/connect/how-elsevier-is-breaking-down-barriers-
to-reproducibility
REPRODUCIBILITY AS QUALITY?

26.
QUALITY AS MORE AUTOMATION

27.
http://blog.booleanbiotech.com/genetic_engine
ering_pipeline_python.html “There are some catches too of course,
especially since it's very early in the
evolution of these tools. If it were the
internet it would be around 1994”

28.
RESEARCH QUESTIONS
1. Does basic lab-based
biomedical research reuse
and assemble existing
methods, or is it primarily
focused on the development
of new techniques?
2. What existing methods are
covered by robotic labs?

29.
RESULTS

30.
DIRECTION: GROUNDING KNOWLEDGE GRAPHS IN
ACTIONS
http://www.researchobject.orghttps://smart-api.info

31.
CONCLUSIONS
▸Knowledge Graphs are crucial for overcoming information overload in research
▸Research has less redundancy than other domains
▸less resources and high diversity
▸challenge: effectively use general knowledge in these domains
▸Quality is central
▸turn towards processes and reproducibility as foundations