1. Knowledge Graph Maintenance
Prof. Paul Groth | @pgroth | pgroth.com | indelab.org
Thanks to Daniel Daza, Thiviyan Thanapalsingam and Frank van Harmelen
Knowledge Graph Conference 2020

2. Roads
and Bridges:The Unseen Labor Behind
Our Digital Infrastructure
W R I T T E N B Y
Nadia Eghbal
Source:
https://www.fordfoundation.org/work/learning/research-reports/roads-and-bridges-the-unseen-labor-behind-our-
digital-infrastructure/

3. Source:
Azzaoui, K., Jacoby, E., Senger, S., Rodríguez, E. C., Loza, M., Zdrazil, B., … Ecker, G. F. (2013). Scientific
competency questions as the basis for semantically enriched open pharmacological space development. Drug
Discovery Today, 18(17–18), 843–852. https://doi.org/10.1016/j.drudis.2013.05.008

5. Source:
https://www.biocuration2019.org/about

6. Source:
https://www.wired.com/story/inside-the-alexa-friendly-world-of-wikidata/

7. Source:
https://stats.wikimedia.org/v2/#/en.wikipedia.org/contributing/user-edits/normal||2001-01-01~2019-09-01|~total|

8. Crowdsourcing
100,000s of hand annotated examples
The TAC Relation Extraction Dataset
Source:
Zhang, Yuhao, et al. "Position-aware attention and supervised data improve slot filling." Proceedings of the 2017
Conference on Empirical Methods in Natural Language Processing. 2017.
Karen Fort, Gilles Adda, Kevin Bretonnel Cohen. Amazon Mechanical Turk: Gold Mine or Coal Mine?. Computational
Linguistics, Massachusetts Institute of Technology Press (MIT Press), 2011, pp.413-420. 10.1162/COLI_a_00057

9. Data Work == People Work

10. Concept1
Concept2 Concept3
KOS
Professional
Curators
Literature
Software
Non-professional
contributors
1. dealing with changing cultural and societal
norms, specifically to address or correct bias;
2. political influence
3. new concepts and terminology arising from
discoveries or change in perspective within a
technical/scientific community
4. gardening
5. incremental contributorship
6. progressive formalization
7. software and automation
8. integration of large numbers of data sources
9. variance in algorithm training data
Data
⚐Society & Politics
(4, 5, 6)
(7, 8, 9)
(3)
(1, 2)
Source:
Michael Lauruhn and Paul Groth.
“Sources of Change for Modern Knowledge Organization Systems." Knowledge Organization 43, no. 8 (2016).

11. Apply ML

12. Content
Universal
schema
Surface form
relations
Structured
relations
Factorization
model
Matrix
Construction
Open
Information
Extraction
Entity
Resolution
Matrix
Factorization
Knowledge
graph
Curation
Predicted
relations
Matrix
Completion
Taxonomy
Triple
Extraction
Concept
Resolution
14M
SD articles
475 M
triples
3.3 million
relations
49 M
relations
~15k ->
1M
entries
Paul Groth, Sujit Pal, Darin McBeath, Brad Allen, Ron Daniel
“Applying Universal Schemas for Domain Specific Ontology Expansion”
5th Workshop on Automated Knowledge Base Construction (AKBC) 2016
Link Prediction & KG Curation

13. Link Prediction

14. Inductive Prediction

15. Inductive Prediction

16. Inductive Prediction

18. Future: Sub-graph Prediction

19. Future: Learning KG Pipelines End-to-End
Paul T. Groth, Antony Scerri, Ron Daniel, Bradley P. Allen:
End-to-End Learning for Answering Structured Queries Directly over Text. DL4KG@ESWC 2019: 57-70

21. Data Work == People Work

22. Knowledge Engineering Revisited
• Knowledge graphs are built ad-hoc
• 100s of components (extractors, scrapers, quality,
scoring, user feedback, ….)
• Unique for each organization
• Existing knowledge engineering theory does not apply:
• Assumes small scale
• Assumes slow change
• People-centric
• Expressive representations
• an updated theory and methods for knowledge
engineering designed for the demands of modern
knowledge graphs

23. knowledgescientist.org

24. Conclusion
• Knowledge graphs require maintenance
• Maintenance is frequently people work
• New ML based methods & new human + machine workflows
• Interested? Happy to talk more
Paul Groth | @pgroth | pgroth.com | indelab.org
Thanks to Daniel Daza, Thiviyan Thanapalsingam and Frank van Harmelen