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Program
Layman’s talk
Committee comes
and grills me
Committee 

retreats
Ceremony
Reception
downstairs
10:00
10:15

11:00
...
Entities of Interest
Entities of Interest
Discovery in Digital Traces
Entities of Interest
Discovery in Digital Traces
Object of study
Entities of Interest
Discovery in Digital Traces
Object of study
Task
Entities of Interest
Discovery in Digital Traces
Object of study
Task Domain
Entities of Interest
Discovery in Digital Traces
Entities of Interest
Discovery in Digital Traces
Entities of Interest
Discovery in Digital Traces
Entities of Interest
Discovery in Digital Traces
Entities of Interest
Discovery in Digital Traces
Entities of Interest
Discovery in Digital Traces
Entities of Interest
Discovery in Digital Traces
• Gain new insights/discover new information
Entities of Interest
Discovery in Digital Traces
• Gain new insights/discover new information
• Answer questions: Who was involved? What
happened? Where, when and why did ...
• Gain new insights/discover new information
• Answer questions: Who was involved? What
happened? Where, when and why did ...
Entities of Interest
Discovery in Digital Traces
Entities of Interest
Discovery in Digital Traces
• “Things with distinct and independent
existence”
Entities of Interest
Discovery in Digital Traces
• “Things with distinct and independent
existence”
• Real-world entities ...
Entities of Interest
Discovery in Digital Traces
• “Things with distinct and independent
existence”
• Real-world entities ...
Entities of Interest
Discovery in Digital Traces
• “Things with distinct and independent
existence”
• Real-world entities ...
Challenges
Challenges
• Language is “noisy”
Challenges
• Language is “noisy”
• “Big Data”
Methods
Methods
• Information Retrieval
Methods
• Information Retrieval
• Searching & finding things
Methods
• Information Retrieval
• Searching & finding things
• Natural Language Processing
Methods
• Information Retrieval
• Searching & finding things
• Natural Language Processing
• (automated) ’understanding’ of...
Methods
• Information Retrieval
• Searching & finding things
• Natural Language Processing
• (automated) ’understanding’ of...
Methods
• Information Retrieval
• Searching & finding things
• Natural Language Processing
• (automated) ’understanding’ of...
Two types of 

Entities of Interest
Two types of 

Entities of Interest
Part 1: Entities in digital traces
Two types of 

Entities of Interest
Part 1: Entities in digital traces
• Content/data
Two types of 

Entities of Interest
Part 1: Entities in digital traces
• Content/data
Part 2: Entities that produce digita...
Two types of 

Entities of Interest
Part 1: Entities in digital traces
• Content/data
Part 2: Entities that produce digita...
Part I
Part 1: Entities in digital traces
Part I
Part 1: Emerging Entities in digital traces
First mention
Wikipedia Page CreatedFirst mention
Wikipedia Page CreatedFirst mention
Are there common temporal patterns in
how entities emerge in online text streams?
Wikipedia Page CreatedFirst mention
Are there common temporal patterns in
how entities emerge in online text streams?Yes!
Can we leverage prior knowledge of entities
to bootstrap the discovery of new entities?
Can we leverage prior knowledge of entities
to bootstrap the discovery of new entities?
Yes!
*****
*****
Can we leverage collective intelligence to
construct entity representations for in-
creased retrieval effectiveness ...
*****
Can we leverage collective intelligence to
construct entity representations for in-
creased retrieval effectiveness ...
Part II
Entities of Interest: Producers of digital traces
Part II
Entities of Interest: Producers of digital traces
Aim: Study and predict real-world activity from
digital traces
Part II
Entities of Interest: Producers of digital traces
Aim: Study and predict real-world activity from
digital traces
T...
d.p.graus@uva.nl z.ren@uva.nl
derijke@uva.nl
d.p.graus@uva.nl z.ren@uva.nl
derijke@uva.nl
d.p.graus@uva.nl z.ren@uva.nl
derijke@uva.nl
Can we predict email communication
through modeling email content and
communi...
d.p.graus@uva.nl z.ren@uva.nl
derijke@uva.nl
Can we predict email communication
through modeling email content and
communi...
Creation times Notification times
Creation times Notification times
Creation times Notification times
Creation times Notification times
Can we identify patterns in the times at
which people ...
Creation times Notification times
Creation times Notification times
Can we identify patterns in the times at
which people ...
In Summary
• Part 1:

We propose methods for analyzing, predicting,
and retrieving emerging entities
• Part 2:

We propose...
Program
Committee comes
and grills me
Committee 

retreats
Ceremony
Reception
downstairs
10:15

11:00

~11:15
~11:30— 

12...
Layman's Talk: Entities of Interest --- Discovery in Digital Traces
Layman's Talk: Entities of Interest --- Discovery in Digital Traces
Layman's Talk: Entities of Interest --- Discovery in Digital Traces
Layman's Talk: Entities of Interest --- Discovery in Digital Traces
Layman's Talk: Entities of Interest --- Discovery in Digital Traces
Layman's Talk: Entities of Interest --- Discovery in Digital Traces
Layman's Talk: Entities of Interest --- Discovery in Digital Traces
Layman's Talk: Entities of Interest --- Discovery in Digital Traces
Layman's Talk: Entities of Interest --- Discovery in Digital Traces
Layman's Talk: Entities of Interest --- Discovery in Digital Traces
Layman's Talk: Entities of Interest --- Discovery in Digital Traces
Layman's Talk: Entities of Interest --- Discovery in Digital Traces
Layman's Talk: Entities of Interest --- Discovery in Digital Traces
Layman's Talk: Entities of Interest --- Discovery in Digital Traces
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Slides of the 10 min layman's talk that preceded my PhD defence. In this talk I summarize ~4yrs of research in 10 minutes, so it's a very high-level overview.

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Layman's Talk: Entities of Interest --- Discovery in Digital Traces

  1. 1. Program Layman’s talk Committee comes and grills me Committee 
 retreats Ceremony Reception downstairs 10:00 10:15
 11:00
 ~11:15 ~11:30— 
 12:30

  2. 2. Entities of Interest
  3. 3. Entities of Interest Discovery in Digital Traces
  4. 4. Entities of Interest Discovery in Digital Traces Object of study
  5. 5. Entities of Interest Discovery in Digital Traces Object of study Task
  6. 6. Entities of Interest Discovery in Digital Traces Object of study Task Domain
  7. 7. Entities of Interest Discovery in Digital Traces
  8. 8. Entities of Interest Discovery in Digital Traces
  9. 9. Entities of Interest Discovery in Digital Traces
  10. 10. Entities of Interest Discovery in Digital Traces
  11. 11. Entities of Interest Discovery in Digital Traces
  12. 12. Entities of Interest Discovery in Digital Traces
  13. 13. Entities of Interest Discovery in Digital Traces
  14. 14. • Gain new insights/discover new information Entities of Interest Discovery in Digital Traces
  15. 15. • Gain new insights/discover new information • Answer questions: Who was involved? What happened? Where, when and why did it happen? Entities of Interest Discovery in Digital Traces
  16. 16. • Gain new insights/discover new information • Answer questions: Who was involved? What happened? Where, when and why did it happen? Entities of Interest Discovery in Digital Traces
  17. 17. Entities of Interest Discovery in Digital Traces
  18. 18. Entities of Interest Discovery in Digital Traces • “Things with distinct and independent existence”
  19. 19. Entities of Interest Discovery in Digital Traces • “Things with distinct and independent existence” • Real-world entities central to answering 5 W’s.
  20. 20. Entities of Interest Discovery in Digital Traces • “Things with distinct and independent existence” • Real-world entities central to answering 5 W’s.
  21. 21. Entities of Interest Discovery in Digital Traces • “Things with distinct and independent existence” • Real-world entities central to answering 5 W’s.
  22. 22. Challenges
  23. 23. Challenges • Language is “noisy”
  24. 24. Challenges • Language is “noisy” • “Big Data”
  25. 25. Methods
  26. 26. Methods • Information Retrieval
  27. 27. Methods • Information Retrieval • Searching & finding things
  28. 28. Methods • Information Retrieval • Searching & finding things • Natural Language Processing
  29. 29. Methods • Information Retrieval • Searching & finding things • Natural Language Processing • (automated) ’understanding’ of language
  30. 30. Methods • Information Retrieval • Searching & finding things • Natural Language Processing • (automated) ’understanding’ of language • Machine Learning
  31. 31. Methods • Information Retrieval • Searching & finding things • Natural Language Processing • (automated) ’understanding’ of language • Machine Learning • Using programs that ‘learn’ to do something
  32. 32. Two types of 
 Entities of Interest
  33. 33. Two types of 
 Entities of Interest Part 1: Entities in digital traces
  34. 34. Two types of 
 Entities of Interest Part 1: Entities in digital traces • Content/data
  35. 35. Two types of 
 Entities of Interest Part 1: Entities in digital traces • Content/data Part 2: Entities that produce digital traces
  36. 36. Two types of 
 Entities of Interest Part 1: Entities in digital traces • Content/data Part 2: Entities that produce digital traces • Context/metadata
  37. 37. Part I Part 1: Entities in digital traces
  38. 38. Part I Part 1: Emerging Entities in digital traces
  39. 39. First mention
  40. 40. Wikipedia Page CreatedFirst mention
  41. 41. Wikipedia Page CreatedFirst mention Are there common temporal patterns in how entities emerge in online text streams?
  42. 42. Wikipedia Page CreatedFirst mention Are there common temporal patterns in how entities emerge in online text streams?Yes!
  43. 43. Can we leverage prior knowledge of entities to bootstrap the discovery of new entities?
  44. 44. Can we leverage prior knowledge of entities to bootstrap the discovery of new entities? Yes!
  45. 45. *****
  46. 46. ***** Can we leverage collective intelligence to construct entity representations for in- creased retrieval effectiveness of entities of interest?
  47. 47. ***** Can we leverage collective intelligence to construct entity representations for in- creased retrieval effectiveness of entities of interest? Yes!
  48. 48. Part II Entities of Interest: Producers of digital traces
  49. 49. Part II Entities of Interest: Producers of digital traces Aim: Study and predict real-world activity from digital traces
  50. 50. Part II Entities of Interest: Producers of digital traces Aim: Study and predict real-world activity from digital traces Two case-studies
  51. 51. d.p.graus@uva.nl z.ren@uva.nl derijke@uva.nl
  52. 52. d.p.graus@uva.nl z.ren@uva.nl derijke@uva.nl
  53. 53. d.p.graus@uva.nl z.ren@uva.nl derijke@uva.nl Can we predict email communication through modeling email content and communication graph properties?
  54. 54. d.p.graus@uva.nl z.ren@uva.nl derijke@uva.nl Can we predict email communication through modeling email content and communication graph properties? Yes!
  55. 55. Creation times Notification times Creation times Notification times
  56. 56. Creation times Notification times Creation times Notification times Can we identify patterns in the times at which people create reminders, and, via notification times, when the associated tasks are to be executed?
  57. 57. Creation times Notification times Creation times Notification times Can we identify patterns in the times at which people create reminders, and, via notification times, when the associated tasks are to be executed? Yes!
  58. 58. In Summary • Part 1:
 We propose methods for analyzing, predicting, and retrieving emerging entities • Part 2:
 We propose methods for predicting future activity by leveraging digital traces.
  59. 59. Program Committee comes and grills me Committee 
 retreats Ceremony Reception downstairs 10:15
 11:00
 ~11:15 ~11:30— 
 12:30


Slides of the 10 min layman's talk that preceded my PhD defence. In this talk I summarize ~4yrs of research in 10 minutes, so it's a very high-level overview.

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