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Computationally Tracing Concepts
Through Time and Space
Marieke van Erp

merpeltje
D I G I TA L H U M A N I T I E S L A B
D I G I TA L H U M A N I T I E S L A B
Overview of this talk
• Big (text) Data & Humanities
• Tracing concepts
• Entity spaces
• New horizons
• Wrapping up
D I G I TA L H U M A N I T I E S L A B
Big Data & Humanities
• Digitised archives are enabling new types
of research
• Dutch National Library: 100+ million
newspaper, book & magazine pages
• Chronicling America: 100,000
newspaper pages
• Amsterdam City Archives: 160,000
notary deeds
• Bibliothèque Nationale de Luxembourg:
800,000 pages
• & many more sources
D I G I TA L H U M A N I T I E S L A B
Zooming in & Zooming out
• Qualitative methods often filter down
to individual records or pages
• Quantitative methods started
scratching the surface
• KNAW HuC focuses on bridging the
gap between quantitative &
qualitative analyses through
advancing natural language
processing and semantic web
methods
Image source: https://upload.wikimedia.org/wikipedia/commons/b/b5/MediaWiki_flame_graph_screenshot_2014-12-15_22.png
D I G I TA L H U M A N I T I E S L A B
Digital Humanities
• Involves the understanding of
these cultural heritage data.
• Methods involving Natural
Language Processing supported
by Knowledge Graphs have
entered the humanities research
community (Meroño-Peñuela et
al.)
Tabea Tietz et al. Challenges of Knowledge Graph Evolution from an NLP Perspective. WHiSe Workshop @ ESWC 2020
D I G I TA L H U M A N I T I E S L A B
Who has the biggest sweet tooth?
• Sugar consumption patterns
are difficult to trace
• Historical apple pie recipes can
serve as a proxy
• Apple pastries are common in
many cultures
Marieke van Erp & Ulbe Bosma: Divergent patterns of sugar consumption in the wake of the Industrial Revolution: an analysis on the basis of
apple pie recipes. Forthcoming
D I G I TA L H U M A N I T I E S L A B
Analysing historical recipes
• Differences in availability of
digitised sources
• Digitisation artefacts hamper
automatic analysis
• Normalisation of quantities is
needed
• Combine quantitative &
qualitative methods
Marieke van Erp & Ulbe Bosma: Divergent patterns of sugar consumption in the wake of the Industrial Revolution: an analysis on the basis of
apple pie recipes. (Forthcoming)
Image source: https://en.wikipedia.org/wiki/Apple_pie#/media/File:For_to_Make_Tartys_in_Applis_(1381).gif
D I G I TA L H U M A N I T I E S L A B
Comparing Ingredients in Dutch and American Apple Pie Recipes
Tabea Tietz et al. Challenges of Knowledge Graph Evolution from an NLP Perspective. WHiSe Workshop @ ESWC 2020
D I G I TA L H U M A N I T I E S L A B
Comparing sugar quantities in Dutch, American, French and German apple pie recipes
Marieke van Erp & Ulbe Bosma: Divergent patterns of sugar consumption in the wake of the Industrial Revolution: an analysis on the basis of apple pie recipes. (Forthcoming)
D I G I TA L H U M A N I T I E S L A B
What is an apple pie?
• The real world is constantly
changing
• Knowledge that was considered
true at one point in time in a
specific cultural and spa7al
setting may not be true in
another context
• Concepts evolve
Tabea Tietz et al. Challenges of Knowledge Graph Evolution from an NLP Perspective. WHiSe Workshop @ ESWC 2020
D I G I TA L H U M A N I T I E S L A B
Cultural Context
● What is considered as true in
one cultural setting may not be
in another.
● Apfelstrudel == apple pie?
Tabea Tietz et al. Challenges of Knowledge Graph Evolution from an NLP Perspective. WHiSe Workshop @ ESWC 2020
How can we store this type of information at scale?
D I G I TA L H U M A N I T I E S L A B
Concept modelling
• Computer Science: Knowledge
Representation/Semantic Web
• Long history: at least since
Aristotle
• Machine readable knowledge was
Sir Tim Berners-Lee’s intent when
he developed the World Wide Web
• To date, we have several large
scale knowledge graphs such as
DBpedia and Wikidata
Image source: https://upload.wikimedia.org/wikipedia/commons/c/c6/Complexity_vs._orderliness.png
D I G I TA L H U M A N I T I E S L A B
Knowledge Graphs
• Represent what we consider
true about parts of the world
• Are created and maintained to
continuously compose
knowledge (Bonatti et al.).
Tabea Tietz et al. Challenges of Knowledge Graph Evolution from an NLP Perspective. WHiSe Workshop @ ESWC 2020
D I G I TA L H U M A N I T I E S L A B
But:
• Knowledge Graphs are often
static and only reflect one
snippet of reality
• This static representation of the
real world is a problem when
attempting to understand
historical descriptions of
concepts (Bonatti et al., Tasnim
et al.)
Tabea Tietz et al. Challenges of Knowledge Graph Evolution from an NLP Perspective. WHiSe Workshop @ ESWC 2020
D I G I TA L H U M A N I T I E S L A B
Concepts
• Are manifested in our cultures’
norms and values
• Are documented through
photographs, newspapers,
books, music, film,
advertisements.
Tabea Tietz et al. Challenges of Knowledge Graph Evolution from an NLP Perspective. WHiSe Workshop @ ESWC 2020
D I G I TA L H U M A N I T I E S L A B
Spatio-temporal context
● Distinguish the spatio-
temporal metadata of the
concept itself and the
metadata of its source
● Trace the evolution of the
concept over time and
geographic regions
Tabea Tietz et al. Challenges of Knowledge Graph Evolution from an NLP Perspective. WHiSe Workshop @ ESWC 2020
D I G I TA L H U M A N I T I E S L A B
Units
● Modern units
○ imperial vs. metric system (lbs,
kg)
● Historical units
○ ell, zentner
● Natural language description of
measurements
○ “a load of butter”, “a plate of
apples”
Tabea Tietz et al. Challenges of Knowledge Graph Evolution from an NLP Perspective. WHiSe Workshop @ ESWC 2020
D I G I TA L H U M A N I T I E S L A B
Concept modelling
● How broad or narrow should
the ontology be modeled to fit
the concept but also capture
its changes over time?
● What are the properties that
define a concept across the
spatio-temporal and cultural
context?
Tabea Tietz et al. Challenges of Knowledge Graph Evolution from an NLP Perspective. WHiSe Workshop @ ESWC 2020
Entity spaces
D I G I TA L H U M A N I T I E S L A B
Language & Meaning
• Human language is incredibly flexible and
efficient
• We can use the term ‘sugar’ to refer to
• the sugar industry (a sour day for sugar)
• to particular instances of sugar (shall I
put some sugar in?)
• nutritional information (sugar and fiber
intake)
• commodities (grain and sugar are
produced)
• How can computers make sense of this?
Marieke van Erp & Paul Groth (2020) Towards Entity Spaces. In: Proceedings of The 12th Language Resources and Evaluation
Conference (LREC’2020)
D I G I TA L H U M A N I T I E S L A B
Proxy for Entity Spaces
Marieke van Erp & Paul Groth (2020) Towards Entity Spaces. In: Proceedings of The 12th Language Resources and Evaluation Conference (LREC’2020)
D I G I TA L H U M A N I T I E S L A B
Tolerant Entity Linking
• Not every meaning of an entity
or concept is represented in a
knowledge base
• We argue that a link to an entity
space is better than no link
• ‘good enough
interpretation’ (Poesio et al.)
• Proof of concept shows increase
in recall for 8 out of 13 datasets
Marieke van Erp & Paul Groth (2020) Towards Entity Spaces. In: Proceedings of The 12th Language Resources and Evaluation
Conference (LREC’2020)
D I G I TA L H U M A N I T I E S L A B
Next steps
• Extending entity spaces beyond
Wikipedia
• Structuring concepts within
entity spaces
• Add temporal dimension
• Intangible concepts
• Scale up
Marieke van Erp & Paul Groth (2020) Towards Entity Spaces. In: Proceedings of The 12th Language Resources and Evaluation
Conference (LREC’2020)
D I G I TA L H U M A N I T I E S L A B
New Horizons
• Complex concepts have
multiple dimensions
• Dimensions may go beyond a
single discipline
• Recognising, modelling & using
concepts and knowledge
graphs require team work
D I G I TA L H U M A N I T I E S L A B
Unexpected Crews
• Within the KNAW Humanities
Cluster, we harbour
(computational) linguists,
historians, literature scientists,
ethnologists, developers, network
specialists, digital humanists…
• Different disciplines find each
other on intersection of topics/
data/methods
• Use your network!
D I G I TA L H U M A N I T I E S L A B
Wrapping Up
• Text analysis and knowledge
representation are becoming
more important to humanities
research
• Big challenges for complex
information extraction and
modelling
• Interdisciplinary collaboration is
needed
http://dhlab.nl
Acknowledgments:
Adina Nerghes, Eleonora Marzi,
Fabio Mariani, Harald Sack,
ISWS Summer School, Lientje
Maas, Mehwish Alam, Melvin
Wevers, Mortaza Alinam, Paul
Groth, Tabea Tietz, Ulbe Bosma
& Wouter van den Berg
References
• Tabea Tietz, Mehwish Alam, Harald Sack and Marieke van Erp (2020) Challenges of Knowledge
Graph Evolution from an NLP Perspective. WHiSe Workshop @ ESWC 2020
• Marieke van Erp & Paul Groth (2020) Towards Entity Spaces. In: Proceedings of The 12th Language
Resources and Evaluation Conference (LREC’2020)
• Marieke van Erp & Ulbe Bosma: Divergent patterns of sugar consumption in the wake of the
Industrial Revolution: an analysis on the basis of apple pie recipes. (Forthcoming)
• Piero Andrea Bonatti, Stefan Decker, Axel Polleres and Valentina Presutti (2019) Knowledge Graphs:
New Directions for Knowledge Representation on the Semantic Web. Dagstuhl Seminar 18371).
Dagstuhl Reports 8(9), 29–111 (2019). https://doi.org/10.4230/DagRep.8.9.29
• Albert Meroño-Peñuela, Ashkan Ashkpour, Marieke van Erp, Kees Mandemakers, Leen Breure,
Andrea Scharnhorst, Stefan Schlobach, Frank van Harmelen (2015) Semantic technologies for
historical research: A survey. In: Semantic Web Journal
• Mayesha Tasnim, Diego Collarana, Damien Graux, Fabrizio Orlandi and Maria-Esther Vidal (2019)
Summarizing Entity Temporal Evolution in Knowledge Graphs. In: Companion Proceedings of The
2019 World Wide Web Conference
•

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Computationally Tracing Concepts Through Time and Space

  • 1. Computationally Tracing Concepts Through Time and Space Marieke van Erp merpeltje D I G I TA L H U M A N I T I E S L A B
  • 2. D I G I TA L H U M A N I T I E S L A B Overview of this talk • Big (text) Data & Humanities • Tracing concepts • Entity spaces • New horizons • Wrapping up
  • 3. D I G I TA L H U M A N I T I E S L A B Big Data & Humanities • Digitised archives are enabling new types of research • Dutch National Library: 100+ million newspaper, book & magazine pages • Chronicling America: 100,000 newspaper pages • Amsterdam City Archives: 160,000 notary deeds • Bibliothèque Nationale de Luxembourg: 800,000 pages • & many more sources
  • 4. D I G I TA L H U M A N I T I E S L A B Zooming in & Zooming out • Qualitative methods often filter down to individual records or pages • Quantitative methods started scratching the surface • KNAW HuC focuses on bridging the gap between quantitative & qualitative analyses through advancing natural language processing and semantic web methods Image source: https://upload.wikimedia.org/wikipedia/commons/b/b5/MediaWiki_flame_graph_screenshot_2014-12-15_22.png
  • 5. D I G I TA L H U M A N I T I E S L A B Digital Humanities • Involves the understanding of these cultural heritage data. • Methods involving Natural Language Processing supported by Knowledge Graphs have entered the humanities research community (Meroño-Peñuela et al.) Tabea Tietz et al. Challenges of Knowledge Graph Evolution from an NLP Perspective. WHiSe Workshop @ ESWC 2020
  • 6. D I G I TA L H U M A N I T I E S L A B Who has the biggest sweet tooth? • Sugar consumption patterns are difficult to trace • Historical apple pie recipes can serve as a proxy • Apple pastries are common in many cultures Marieke van Erp & Ulbe Bosma: Divergent patterns of sugar consumption in the wake of the Industrial Revolution: an analysis on the basis of apple pie recipes. Forthcoming
  • 7. D I G I TA L H U M A N I T I E S L A B Analysing historical recipes • Differences in availability of digitised sources • Digitisation artefacts hamper automatic analysis • Normalisation of quantities is needed • Combine quantitative & qualitative methods Marieke van Erp & Ulbe Bosma: Divergent patterns of sugar consumption in the wake of the Industrial Revolution: an analysis on the basis of apple pie recipes. (Forthcoming) Image source: https://en.wikipedia.org/wiki/Apple_pie#/media/File:For_to_Make_Tartys_in_Applis_(1381).gif
  • 8. D I G I TA L H U M A N I T I E S L A B Comparing Ingredients in Dutch and American Apple Pie Recipes Tabea Tietz et al. Challenges of Knowledge Graph Evolution from an NLP Perspective. WHiSe Workshop @ ESWC 2020
  • 9.
  • 10. D I G I TA L H U M A N I T I E S L A B Comparing sugar quantities in Dutch, American, French and German apple pie recipes Marieke van Erp & Ulbe Bosma: Divergent patterns of sugar consumption in the wake of the Industrial Revolution: an analysis on the basis of apple pie recipes. (Forthcoming)
  • 11. D I G I TA L H U M A N I T I E S L A B What is an apple pie? • The real world is constantly changing • Knowledge that was considered true at one point in time in a specific cultural and spa7al setting may not be true in another context • Concepts evolve Tabea Tietz et al. Challenges of Knowledge Graph Evolution from an NLP Perspective. WHiSe Workshop @ ESWC 2020
  • 12. D I G I TA L H U M A N I T I E S L A B Cultural Context ● What is considered as true in one cultural setting may not be in another. ● Apfelstrudel == apple pie? Tabea Tietz et al. Challenges of Knowledge Graph Evolution from an NLP Perspective. WHiSe Workshop @ ESWC 2020
  • 13. How can we store this type of information at scale?
  • 14. D I G I TA L H U M A N I T I E S L A B Concept modelling • Computer Science: Knowledge Representation/Semantic Web • Long history: at least since Aristotle • Machine readable knowledge was Sir Tim Berners-Lee’s intent when he developed the World Wide Web • To date, we have several large scale knowledge graphs such as DBpedia and Wikidata Image source: https://upload.wikimedia.org/wikipedia/commons/c/c6/Complexity_vs._orderliness.png
  • 15. D I G I TA L H U M A N I T I E S L A B Knowledge Graphs • Represent what we consider true about parts of the world • Are created and maintained to continuously compose knowledge (Bonatti et al.). Tabea Tietz et al. Challenges of Knowledge Graph Evolution from an NLP Perspective. WHiSe Workshop @ ESWC 2020
  • 16. D I G I TA L H U M A N I T I E S L A B But: • Knowledge Graphs are often static and only reflect one snippet of reality • This static representation of the real world is a problem when attempting to understand historical descriptions of concepts (Bonatti et al., Tasnim et al.) Tabea Tietz et al. Challenges of Knowledge Graph Evolution from an NLP Perspective. WHiSe Workshop @ ESWC 2020
  • 17. D I G I TA L H U M A N I T I E S L A B Concepts • Are manifested in our cultures’ norms and values • Are documented through photographs, newspapers, books, music, film, advertisements. Tabea Tietz et al. Challenges of Knowledge Graph Evolution from an NLP Perspective. WHiSe Workshop @ ESWC 2020
  • 18. D I G I TA L H U M A N I T I E S L A B Spatio-temporal context ● Distinguish the spatio- temporal metadata of the concept itself and the metadata of its source ● Trace the evolution of the concept over time and geographic regions Tabea Tietz et al. Challenges of Knowledge Graph Evolution from an NLP Perspective. WHiSe Workshop @ ESWC 2020
  • 19. D I G I TA L H U M A N I T I E S L A B Units ● Modern units ○ imperial vs. metric system (lbs, kg) ● Historical units ○ ell, zentner ● Natural language description of measurements ○ “a load of butter”, “a plate of apples” Tabea Tietz et al. Challenges of Knowledge Graph Evolution from an NLP Perspective. WHiSe Workshop @ ESWC 2020
  • 20. D I G I TA L H U M A N I T I E S L A B Concept modelling ● How broad or narrow should the ontology be modeled to fit the concept but also capture its changes over time? ● What are the properties that define a concept across the spatio-temporal and cultural context? Tabea Tietz et al. Challenges of Knowledge Graph Evolution from an NLP Perspective. WHiSe Workshop @ ESWC 2020
  • 22. D I G I TA L H U M A N I T I E S L A B Language & Meaning • Human language is incredibly flexible and efficient • We can use the term ‘sugar’ to refer to • the sugar industry (a sour day for sugar) • to particular instances of sugar (shall I put some sugar in?) • nutritional information (sugar and fiber intake) • commodities (grain and sugar are produced) • How can computers make sense of this? Marieke van Erp & Paul Groth (2020) Towards Entity Spaces. In: Proceedings of The 12th Language Resources and Evaluation Conference (LREC’2020)
  • 23. D I G I TA L H U M A N I T I E S L A B Proxy for Entity Spaces Marieke van Erp & Paul Groth (2020) Towards Entity Spaces. In: Proceedings of The 12th Language Resources and Evaluation Conference (LREC’2020)
  • 24. D I G I TA L H U M A N I T I E S L A B Tolerant Entity Linking • Not every meaning of an entity or concept is represented in a knowledge base • We argue that a link to an entity space is better than no link • ‘good enough interpretation’ (Poesio et al.) • Proof of concept shows increase in recall for 8 out of 13 datasets Marieke van Erp & Paul Groth (2020) Towards Entity Spaces. In: Proceedings of The 12th Language Resources and Evaluation Conference (LREC’2020)
  • 25. D I G I TA L H U M A N I T I E S L A B Next steps • Extending entity spaces beyond Wikipedia • Structuring concepts within entity spaces • Add temporal dimension • Intangible concepts • Scale up Marieke van Erp & Paul Groth (2020) Towards Entity Spaces. In: Proceedings of The 12th Language Resources and Evaluation Conference (LREC’2020)
  • 26. D I G I TA L H U M A N I T I E S L A B New Horizons • Complex concepts have multiple dimensions • Dimensions may go beyond a single discipline • Recognising, modelling & using concepts and knowledge graphs require team work
  • 27. D I G I TA L H U M A N I T I E S L A B Unexpected Crews • Within the KNAW Humanities Cluster, we harbour (computational) linguists, historians, literature scientists, ethnologists, developers, network specialists, digital humanists… • Different disciplines find each other on intersection of topics/ data/methods • Use your network!
  • 28. D I G I TA L H U M A N I T I E S L A B Wrapping Up • Text analysis and knowledge representation are becoming more important to humanities research • Big challenges for complex information extraction and modelling • Interdisciplinary collaboration is needed
  • 29. http://dhlab.nl Acknowledgments: Adina Nerghes, Eleonora Marzi, Fabio Mariani, Harald Sack, ISWS Summer School, Lientje Maas, Mehwish Alam, Melvin Wevers, Mortaza Alinam, Paul Groth, Tabea Tietz, Ulbe Bosma & Wouter van den Berg
  • 30. References • Tabea Tietz, Mehwish Alam, Harald Sack and Marieke van Erp (2020) Challenges of Knowledge Graph Evolution from an NLP Perspective. WHiSe Workshop @ ESWC 2020 • Marieke van Erp & Paul Groth (2020) Towards Entity Spaces. In: Proceedings of The 12th Language Resources and Evaluation Conference (LREC’2020) • Marieke van Erp & Ulbe Bosma: Divergent patterns of sugar consumption in the wake of the Industrial Revolution: an analysis on the basis of apple pie recipes. (Forthcoming) • Piero Andrea Bonatti, Stefan Decker, Axel Polleres and Valentina Presutti (2019) Knowledge Graphs: New Directions for Knowledge Representation on the Semantic Web. Dagstuhl Seminar 18371). Dagstuhl Reports 8(9), 29–111 (2019). https://doi.org/10.4230/DagRep.8.9.29 • Albert Meroño-Peñuela, Ashkan Ashkpour, Marieke van Erp, Kees Mandemakers, Leen Breure, Andrea Scharnhorst, Stefan Schlobach, Frank van Harmelen (2015) Semantic technologies for historical research: A survey. In: Semantic Web Journal • Mayesha Tasnim, Diego Collarana, Damien Graux, Fabrizio Orlandi and Maria-Esther Vidal (2019) Summarizing Entity Temporal Evolution in Knowledge Graphs. In: Companion Proceedings of The 2019 World Wide Web Conference •