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Research Knowledge Graphs at NFDI4DS & GESIS
RKG Workshop
Stefan Dietze, 23.06.2022
NFDI4DS - Consortium
2
FHI
Universität
Leipzig
 Several universities as centers of
excellence in DS and AI.
 Several non-university research
institutes as key contributors.
 Scientific information centers
and infrastructure providers as
providers of data and for access to
domain user groups.
● Paradigm shift in computer science towards data- and deep
learning driven methods (DS = fourth scientific paradigm).
● Reproducibility crisis.
○ Study on neural recommender publications at top-tier-
venues finds only 7/18 reproducible and only 1/18 beating
state-of-the-art. [Dacrema et al., RecSys2019]
● Bias and discrimination from learned data-induced biases.
○ Gender classification performance of neural approaches
performs significantly worse for darker skinned women.
[Buolamwini et al., FAccT2018]
○ Health risk of black people consistently underestimated
by predictive algorithms. [Obermeyer et al., Science2019]
Data Science & AI Challenges
3
▪ Research Data
▪ Publications
▪ Code/Scripts
▪ ML Models
▪ Methods
▪ Claims
▪ Metrics
Relations between scientific resources, data, knowledge Research Data Cycle
Provenance & Dependencies of Research Data, Resources, Knowledge
Common questions for researchers
• Which top-tier publications cite which data/method?
(„dataset authority“)
• Which data was used to train/evaluate which method?
Which method to produce what data?
• Which claims are supported/cited/rejected by what
dataset or publication?
▪ Research Data
▪ Publications
▪ Code/Scripts
▪ ML Models
▪ Methods
▪ Claims
▪ Metrics
Relations between scientific resources, data, knowledge
Provenance & Dependencies of Research Data, Resources, Knowledge
Challenges
• Data & metadata about resources and concepts not
represented in structured, machine-interpretable,
integrated manner (hidden in publications, web pages
etc)
• Persistent identifiers (e.g. DOIs) used inconsistently
(e.g. on publications/datasets)
• Relations and semantics not explicit
• Reproducibility crisis in CS/DS/AI
▪ Research Data
▪ Publications
▪ Code/Scripts
▪ ML Models
▪ Methods
▪ Claims
▪ Metrics
Relations between scientific resources, data, knowledge
Provenance & Dependencies of Research Data, Resources, Knowledge
• Data interoperability and reuse through established W3C standards for data
sharing (on the Web), e.g. RDF, JSON, shared vocabularies
(e.g. schema.org, DCAT, DDI), APIs for data reuse and linking
• Making links between resources and concepts explicit & machine-
interpretable
(e.g. which publications cite what dataset?)
• Consistent use of persisent IDs (e.g. URIs, DOIs) across all data, e.g.
concepts, resources etc („DOIs for all“)
• Partners in NFDI4DS/TA2 (“RKGs”)
Knowledge Graphs for FAIR Research Data in NFDI4DS
Resources
▪ Datasets
▪ Publications
▪ Code
▪ Software
Concepts
▪ Terms &
Definitions
▪ Claims
▪ Methods
▪ Topics
▪ Entities
GESIS & TIB RKGs in NFDI4DS
10
• Community
• Expert-curation/annotation
workflow/tools
• Focus point & hub
• PIDs
https://www.nfdi4datascience.de/
• Large-scale knowledge
graphs
• Automated deep learning-
based methods for
extracting KGs
• Populating ORKG
Resources
▪ Datasets
▪ Publications
▪ Code
▪ Software
Concepts
▪ Terms &
Definitions
▪ Claims
▪ Methods
▪ Topics
▪ Entities
11
Research KGs in Practice: integrated search @ GESIS
https://search.gesis.org/
Dataset
Rel. Publications
12
From publications to machine-interpretable metadata KGs
Disambiguation of dataset & software/script citations
https://data.gesis.org/softwarekg
▪ Training deep learning-
based model for extraction
software & data references
in large-scale data
(3.5 M publications)
▪ Data lifting into KG
▪ 300+ M triples / statements
▪ Search across
data/software/publications
(GESIS Search)
From publications to machine-interpretable metadata KGs
Understanding scientific software/data usage
https://data.gesis.org/softwarekg
(Schindler et al., CIKM2021)
▪ Understanding SW
usage, citation habits
and their evolution
across disciplines
▪ Rise of data science =
rise of software usage
https://data.gesis.org/softwarekg
▪ Top adopters of data
science/AI/software…
From publications to machine-interpretable metadata KGs
Understanding scientific software/data usage
https://data.gesis.org/softwarekg
▪ Top adopters of data
science/AI/software…
▪ …follow the worst
citation habits
From publications to machine-interpretable metadata KGs
Understanding scientific software/data usage
http://dbpedia.org/resource/Tim_Berners-Lee
wna:positive-emotion
onyx:Intensity "0.75"
onyx:Intensity "0.0"
http://dbpedia.org/resource/Solid
wna:negative-emotion
From social media to machine-interpretable research data KGs
Building a public research knowledge graph from Twitter data
https://data.gesis.org/tweetskb
From social media to machine-interpretable research data KGs
https://data.gesis.org/tweetskb
TweetsKB – a large-scale research KG of societal opinions
▪ Harvesting & archiving of 10 Billion tweets
(permanent collection from Twitter 1% sample since
2013)
▪ Information extraction pipeline to build a KG of
entities, interactions & sentiments
(distributed batch processing via Hadoop
Map/Reduce)
o Entity linking with knowledge graph/DBpedia
(“president”/“potus”/”trump” =>
dbp:DonaldTrump)
o Sentiment analysis/annotation
o Geotagging
o Lifting into knowledge graph schema
▪ Public, privacy-aware, large-scale research corpus
of public opinions and their evolution
=> interdisciplinary research
P. Fafalios, V. Iosifidis, E. Ntoutsi, and S. Dietze, TweetsKB: A Public
and Large-Scale RDF Corpus of Annotated Tweets, ESWC'18.
RKG-based social science research using TweetsKB
https://dd4p.gesis.org
Investigating Vaccine Hesitancy in DACH countries
Germany suspends
vaccinations with Astra
Zeneca
Twitter discourse zu “Impfbereitschaft”
RKG-based social science research using TweetsKB
Investigating Vaccine Hesitancy in DACH countries
https://dd4p.gesis.org
RKG-based discourse analysis using TweetsKB
Vaccine Hesitancy– key topics in “safety” category
„Schwangerschaft“ „Kimmich“
„Alter“
„Nebenwirkungen“
„Herzinfarkt“
„Zulassung“
https://dd4p.gesis.org
Summary: Research KGs @ GESIS
21
Tools for constructing scholarly knowledge graphs
● NLP and deep learning-powered methods for extracting large-scale KGs
about methods, claims, data, software involved in the scientific process
Large-scale scholarly KGs, e.g.
● KGs about scholarly use of software & research data
(e.g. SoftwareKG: 1.8 M disambiguated software mentions extracted
from 3 M publications, https://data.gesis.org/softwarekg/)
● Web mined KGs of social science research data, e.g. public opinions,
claims and attitudes expressed on social media
(e.g. TweetsKB: > 10 Bn semantically annotated tweets, sentiments,
https://data.gesis.org/tweetskb)
Semantic Search powered by KGs and related tools
● RKG-powered search across scholarly publications, datasets, methods
and their relations (e.g. GESIS Search, https://search.gesis.org)
https://gesis.org/en/kts
https://search.gesis.org
Outlook: a joint Research KG in NFDI4DS
22
Resources
▪ Datasets
▪ Publications
▪ Code
▪ Software
Concepts
▪ Terms &
Definitions
▪ Claims
▪ Methods
▪ Topics
▪ Entities
• Community
• Expert-curation/annotation
workflow/tools
• Focus point & hub
• PIDs
https://www.nfdi4datascience.de/
• Large-scale knowledge
graphs
• Automated deep learning-
based methods for
extracting KGs
• Populating ORKG
23
@stefandietze
http://stefandietze.net

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Research Knowledge Graphs at NFDI4DS & GESIS

  • 1. Research Knowledge Graphs at NFDI4DS & GESIS RKG Workshop Stefan Dietze, 23.06.2022
  • 2. NFDI4DS - Consortium 2 FHI Universität Leipzig  Several universities as centers of excellence in DS and AI.  Several non-university research institutes as key contributors.  Scientific information centers and infrastructure providers as providers of data and for access to domain user groups.
  • 3. ● Paradigm shift in computer science towards data- and deep learning driven methods (DS = fourth scientific paradigm). ● Reproducibility crisis. ○ Study on neural recommender publications at top-tier- venues finds only 7/18 reproducible and only 1/18 beating state-of-the-art. [Dacrema et al., RecSys2019] ● Bias and discrimination from learned data-induced biases. ○ Gender classification performance of neural approaches performs significantly worse for darker skinned women. [Buolamwini et al., FAccT2018] ○ Health risk of black people consistently underestimated by predictive algorithms. [Obermeyer et al., Science2019] Data Science & AI Challenges 3
  • 4. ▪ Research Data ▪ Publications ▪ Code/Scripts ▪ ML Models ▪ Methods ▪ Claims ▪ Metrics Relations between scientific resources, data, knowledge Research Data Cycle Provenance & Dependencies of Research Data, Resources, Knowledge
  • 5. Common questions for researchers • Which top-tier publications cite which data/method? („dataset authority“) • Which data was used to train/evaluate which method? Which method to produce what data? • Which claims are supported/cited/rejected by what dataset or publication? ▪ Research Data ▪ Publications ▪ Code/Scripts ▪ ML Models ▪ Methods ▪ Claims ▪ Metrics Relations between scientific resources, data, knowledge Provenance & Dependencies of Research Data, Resources, Knowledge
  • 6. Challenges • Data & metadata about resources and concepts not represented in structured, machine-interpretable, integrated manner (hidden in publications, web pages etc) • Persistent identifiers (e.g. DOIs) used inconsistently (e.g. on publications/datasets) • Relations and semantics not explicit • Reproducibility crisis in CS/DS/AI ▪ Research Data ▪ Publications ▪ Code/Scripts ▪ ML Models ▪ Methods ▪ Claims ▪ Metrics Relations between scientific resources, data, knowledge Provenance & Dependencies of Research Data, Resources, Knowledge
  • 7. • Data interoperability and reuse through established W3C standards for data sharing (on the Web), e.g. RDF, JSON, shared vocabularies (e.g. schema.org, DCAT, DDI), APIs for data reuse and linking • Making links between resources and concepts explicit & machine- interpretable (e.g. which publications cite what dataset?) • Consistent use of persisent IDs (e.g. URIs, DOIs) across all data, e.g. concepts, resources etc („DOIs for all“) • Partners in NFDI4DS/TA2 (“RKGs”) Knowledge Graphs for FAIR Research Data in NFDI4DS Resources ▪ Datasets ▪ Publications ▪ Code ▪ Software Concepts ▪ Terms & Definitions ▪ Claims ▪ Methods ▪ Topics ▪ Entities
  • 8. GESIS & TIB RKGs in NFDI4DS 10 • Community • Expert-curation/annotation workflow/tools • Focus point & hub • PIDs https://www.nfdi4datascience.de/ • Large-scale knowledge graphs • Automated deep learning- based methods for extracting KGs • Populating ORKG Resources ▪ Datasets ▪ Publications ▪ Code ▪ Software Concepts ▪ Terms & Definitions ▪ Claims ▪ Methods ▪ Topics ▪ Entities
  • 9. 11 Research KGs in Practice: integrated search @ GESIS https://search.gesis.org/ Dataset Rel. Publications
  • 10. 12 From publications to machine-interpretable metadata KGs Disambiguation of dataset & software/script citations https://data.gesis.org/softwarekg ▪ Training deep learning- based model for extraction software & data references in large-scale data (3.5 M publications) ▪ Data lifting into KG ▪ 300+ M triples / statements ▪ Search across data/software/publications (GESIS Search)
  • 11. From publications to machine-interpretable metadata KGs Understanding scientific software/data usage https://data.gesis.org/softwarekg (Schindler et al., CIKM2021) ▪ Understanding SW usage, citation habits and their evolution across disciplines ▪ Rise of data science = rise of software usage
  • 12. https://data.gesis.org/softwarekg ▪ Top adopters of data science/AI/software… From publications to machine-interpretable metadata KGs Understanding scientific software/data usage
  • 13. https://data.gesis.org/softwarekg ▪ Top adopters of data science/AI/software… ▪ …follow the worst citation habits From publications to machine-interpretable metadata KGs Understanding scientific software/data usage
  • 14. http://dbpedia.org/resource/Tim_Berners-Lee wna:positive-emotion onyx:Intensity "0.75" onyx:Intensity "0.0" http://dbpedia.org/resource/Solid wna:negative-emotion From social media to machine-interpretable research data KGs Building a public research knowledge graph from Twitter data https://data.gesis.org/tweetskb
  • 15. From social media to machine-interpretable research data KGs https://data.gesis.org/tweetskb TweetsKB – a large-scale research KG of societal opinions ▪ Harvesting & archiving of 10 Billion tweets (permanent collection from Twitter 1% sample since 2013) ▪ Information extraction pipeline to build a KG of entities, interactions & sentiments (distributed batch processing via Hadoop Map/Reduce) o Entity linking with knowledge graph/DBpedia (“president”/“potus”/”trump” => dbp:DonaldTrump) o Sentiment analysis/annotation o Geotagging o Lifting into knowledge graph schema ▪ Public, privacy-aware, large-scale research corpus of public opinions and their evolution => interdisciplinary research P. Fafalios, V. Iosifidis, E. Ntoutsi, and S. Dietze, TweetsKB: A Public and Large-Scale RDF Corpus of Annotated Tweets, ESWC'18.
  • 16. RKG-based social science research using TweetsKB https://dd4p.gesis.org Investigating Vaccine Hesitancy in DACH countries
  • 17. Germany suspends vaccinations with Astra Zeneca Twitter discourse zu “Impfbereitschaft” RKG-based social science research using TweetsKB Investigating Vaccine Hesitancy in DACH countries https://dd4p.gesis.org
  • 18. RKG-based discourse analysis using TweetsKB Vaccine Hesitancy– key topics in “safety” category „Schwangerschaft“ „Kimmich“ „Alter“ „Nebenwirkungen“ „Herzinfarkt“ „Zulassung“ https://dd4p.gesis.org
  • 19. Summary: Research KGs @ GESIS 21 Tools for constructing scholarly knowledge graphs ● NLP and deep learning-powered methods for extracting large-scale KGs about methods, claims, data, software involved in the scientific process Large-scale scholarly KGs, e.g. ● KGs about scholarly use of software & research data (e.g. SoftwareKG: 1.8 M disambiguated software mentions extracted from 3 M publications, https://data.gesis.org/softwarekg/) ● Web mined KGs of social science research data, e.g. public opinions, claims and attitudes expressed on social media (e.g. TweetsKB: > 10 Bn semantically annotated tweets, sentiments, https://data.gesis.org/tweetskb) Semantic Search powered by KGs and related tools ● RKG-powered search across scholarly publications, datasets, methods and their relations (e.g. GESIS Search, https://search.gesis.org) https://gesis.org/en/kts https://search.gesis.org
  • 20. Outlook: a joint Research KG in NFDI4DS 22 Resources ▪ Datasets ▪ Publications ▪ Code ▪ Software Concepts ▪ Terms & Definitions ▪ Claims ▪ Methods ▪ Topics ▪ Entities • Community • Expert-curation/annotation workflow/tools • Focus point & hub • PIDs https://www.nfdi4datascience.de/ • Large-scale knowledge graphs • Automated deep learning- based methods for extracting KGs • Populating ORKG