Anlässlich unseres 15 jährigen Firmenjubiläums fand am 17. September 2015 ein exklusives B-S-S Digital Workplace Summit statt - mit praxisnahen Berichten und hochkarätigen Speakern zum Thema "Digitaler Arbeitsplatz - wandelnde Werte und intelligente Systeme". http://www.b-s-s.de/unternehmen/digitaler-arbeitsplatz-wandelnde-werte-und-intelligente-systeme

1. Known-Item Search
Matthias Hagen
Bauhaus-Universit¨at Weimar
matthias.hagen@uni-weimar.de
@matthias_hagen
B-S-S Anniversary
Eisenach
September 16, 2015
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2. The scenario
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3. This is not just a problem of philosoraptor!
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4. Known-item search
Re-finding previously
seen/heard items like
Documents
Websites
Emails
Tweets
Movies
Music
Books
TV
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5. Known-item search
Re-finding previously
seen/heard items like
Documents
Websites
Emails
Tweets
Movies
Music
Books
TV
Remarks: Users have some knowledge about their need.
Only very few relevant documents out there.
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6. Problem
How do users search for known items?
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7. Studies on re-finding known items
Web search [Sadeghi et al., ECIR 2015]
[Tyler and Teevan, WSDM 2010]
[Edar at al., CHI 2008]
[Azzopardi et al., SIGIR 2007]
[Teevan, TOIS 2008, UIST 2007]
[Beitzel et al., SIGIR 2003]
Twitter search [Meier and Elsweiler, IIiX 2014]
Email search [Elsweiler et al., SIGIR 2011, ECIR 2011, TOIS 2008]
PIM [Kim and Croft, SIGIR 2010, CIKM 2009]
[Kelly et al., IIiX 2008]
[Blanc-Brude and Scapin, IUI 2007]
[Boardman and Sasse, CHI 2004]
[Dumais et al., SIGIR 2003]
[Barreau and Nardi, SIGCHI Bulletin 1995]
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8. Studies on re-finding known items
Web search [Sadeghi et al., ECIR 2015]
[Tyler and Teevan, WSDM 2010]
[Edar at al., CHI 2008]
[Azzopardi et al., SIGIR 2007]
[Teevan, TOIS 2008, UIST 2007]
[Beitzel et al., SIGIR 2003]
Twitter search [Meier and Elsweiler, IIiX 2014]
Email search [Elsweiler et al., SIGIR 2011, ECIR 2011, TOIS 2008]
PIM [Kim and Croft, SIGIR 2010, CIKM 2009]
[Kelly et al., IIiX 2008]
[Blanc-Brude and Scapin, IUI 2007]
[Boardman and Sasse, CHI 2004]
[Dumais et al., SIGIR 2003]
[Barreau and Nardi, SIGCHI Bulletin 1995]
Problem: Most corpora and queries not freely available.
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9. Exceptions: Known-item query generation
Automatic extraction
1 Select some document
2 Draw most discriminative terms
3 Add random noise
Web [Azzopardi et al., SIGIR 2007]
PIM [Kim and Croft, CIKM 2009]
Email [Elsweiler et al., SIGIR 2011]
Human computation game
1 Select some document
2 Show it to a user for some time
3 Ask for a query retrieving it
top-ranked
PIM [Kim and Croft, SIGIR 2010]
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10. Exceptions: Known-item query generation
Automatic extraction
1 Select some document
2 Draw most discriminative terms
3 Add random noise
Web [Azzopardi et al., SIGIR 2007]
PIM [Kim and Croft, CIKM 2009]
Email [Elsweiler et al., SIGIR 2011]
Human computation game
1 Select some document
2 Show it to a user for some time
3 Ask for a query retrieving it
top-ranked
PIM [Kim and Croft, SIGIR 2010]
Problem: Not really“natural”settings.
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11. Human memory: Not perfect but also not random
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12. Reasons for memory failure?
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13. Reasons for memory failure? Psychology!
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14. Our goal
A large corpus of difficult and realistic known-item needs.
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15. Our goal
A large corpus of difficult and realistic known-item needs.
Remark: Freely available!
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16. The general idea [Hauff et al., IIiX 2012]
1 Fetch known-item questions from Yahoo! Answers
To ensure realistic human information needs
Websites, movies, music, books, TV series
2 Link questions to a large static web crawl
Environment for repeatable research
ClueWeb09 chosen
3 Construct queries from questions
Maybe via crowdsourcing
Not part of this paper
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17. Question acquisition
Querying Yahoo! Answers API:
forgot AND name AND film
forgot AND title AND song
remember AND title AND movie
forgot AND url AND (website OR (web site))
(remember OR forgot) AND (name OR title) AND book
37 such queries in total
24,765 answered questions returned
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18. Question acquisition
Querying Yahoo! Answers API:
forgot AND name AND film
forgot AND title AND song
remember AND title AND movie
forgot AND url AND (website OR (web site))
(remember OR forgot) AND (name OR title) AND book
37 such queries in total
24,765 answered questions returned
Problems: Not all questions are really“answered.”
Not all questions are known-item intents.
Not all questions are linkable to the ClueWeb09.
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19. Corpus cleansing
Answered status
Keep when best answer selected by asker
8,825 questions remain (only about 36% of original crawl)
Known-item status and ClueWeb linkage need manual assessment
Two independent annotators
About 400 hours of work
3,406 questions with known-item information need
2,755 can be linked to ClueWeb09 documents
Only these form our dataset
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20. Corpus cleansing
Answered status
Keep when best answer selected by asker
8,825 questions remain (only about 36% of original crawl)
Known-item status and ClueWeb linkage need manual assessment
Two independent annotators
About 400 hours of work
3,406 questions with known-item information need
2,755 can be linked to ClueWeb09 documents
Only these form our dataset
Problem: Hardly any website questions remained.
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21. ClueWeb09 coverage
Over the years
Question from 2006 2007 2008 2009 2010 2011 2012
Our dataset 68 176 369 701 578 477 364
Coverage 89.5% 92.2% 86.0% 86.2% 79.6% 77.3% 71.9%
Type of associated URL
95% Wikipedia
5% other
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22. Corpus analysis
Initial observation
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23. False memories hinder total recall
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24. False memories in questions
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25. Movie“. . . starts off with a box full of free puppies . . . ”
Question
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26. Movie“. . . starts off with a box full of free puppies . . . ”
Question Actual known item
Note a difference?!
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27. False memories in questions
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28. Movie“. . . Morgan Freeman offers him a job to kill . . . ”
Question
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29. Movie“. . . Morgan Freeman offers him a job to kill . . . ”
Question Actual known item
Note a difference?!
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30. Funny! But these are just a few outliers?!
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31. False memories statistics
At least 240 questions (9% of corpus) contain false memories
Most frequent false memories: Person names!
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32. False memories statistics
At least 240 questions (9% of corpus) contain false memories
Most frequent false memories: Person names!
Remark: Makes me think . . .
Does my mail search take this into account?
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33. Potential usage of the corpus
Observation: False memories hinder good results.
Might even yield zero-result lists!
Retrieval systems should
Detect false memory situations
“Repair”the query
Leave out the false memory or
Replace it with correction
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34. Potential usage of the corpus
Observation: False memories hinder good results.
Might even yield zero-result lists!
Retrieval systems should
Detect false memory situations
“Repair”the query
Leave out the false memory or
Replace it with correction
Our corpus might be a starting point in that direction.
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35. Other fields: False memory implantation
Remark: We are not working on that!
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36. A little scary?!
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37. Let’s finish the talk in a better mood!
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38. You know this song?!
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39. One more hint needed?!
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40. Yes, the Bee Gees!
Ah, ha, ha, ha, steak and a knife, steak and a knife
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41. Some funny false memories really are Mondegreens.
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42. Some funny false memories really are Mondegreens.
. . . that are misheard lyrics.
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43. Almost the end: The take-home messages!
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44. What we have done
Results
2,755 known-item questions
Posted by real human users
Linked to the ClueWeb09
False memories annotated
Often refer to persons
Or song lyrics
Future Work
Enlarge the corpus
Website known-items esp.
Web queries for the questions
False memory detection
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45. What we have (not) done
Results
2,755 known-item questions
Posted by real human users
Linked to the ClueWeb09
False memories annotated
Often refer to persons
Or song lyrics
Future Work
Enlarge the corpus
Website known-items esp.
Web queries for the questions
False memory detection
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46. What we have (not) done
Results
2,755 known-item questions
Posted by real human users
Linked to the ClueWeb09
False memories annotated
Often refer to persons
Or song lyrics
Future Work
Enlarge the corpus
Website known-items esp.
Web queries for the questions
False memory detection
Thank you
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