The document details the proceedings of the kdir09 international conference, which focuses on knowledge hierarchical taxonomy extraction through the mining of topical query sessions for improved information retrieval. It discusses the challenges of query misinterpretation and proposes methods for automatic acquisition of hyponym taxonomies from query logs, emphasizing the importance of understanding user semantics. Various methodologies, results, and future work regarding query session analysis and taxonomy construction are also highlighted.

1. KDIR09
International Conference On Knowledge
Hierachical taxonomy extraction
by mining topical query sessions
Dicovery and Information Retrieval 2009 Miguel Fernández Fernández and Daniel Gayo Avello

3. brittany spears
www.wikipedia.org
horse jumping
auto restoration
auto repair
classic car repair
car supplies
classic car batteries
vintageparts.com
low cost airlines
cheap flights
easyjet.com

4. brittany spears
www.wikipedia.org
horse jumping
auto restoration
auto repair
classic car repair
car supplies
classic car batteries
vintageparts.com
“... a series of
interactions by the
low cost airlines user toward addressing
cheap flights a single information
easyjet.com need...”
Jansen et. al 2007

5. Unfortunatelly, not all Wang and Zhai 2008.
Mining term association patterns from
queries are equally effective search logs for effective query
reformulation.

6. Unfortunatelly, not all Wang and Zhai 2008.
Mining term association patterns from
queries are equally effective search logs for effective query
reformulation.
Mispeci
fication
different people use different
words to discribe the same thing!

7. Unfortunatelly, not all Wang and Zhai 2008.
Mining term association patterns from
queries are equally effective search logs for effective query
reformulation.
Mispeci
fication cification
Underspe
different people use different user has shallow knowledge about
words to discribe the same thing! what he is looking for

8. How can they be mitigated?
Und
e
n rspec
cifi catio ific
Mispe atio
n

10. mispecification (typo)
query suggestion

11. mispecification (typo)
query suggestion
query expansion

12. mispecification (typo)
query suggestion
query expansion
based on clustering

13. mispecification (typo)
query suggestion
query expansion
based on clustering

14. Wuh! Pretty cool, but...
Jargon
Slang
Vague domain knowledge
...are still on the game

15. Not

16. Not
semantic
query sugg
& expansioestion
n

17. How?

18. How?

19. hyponym |ˈhīpəˌnim|
a word of more specific meaning than a
general or superordinate term applicable to it.

20. hyponymy|ˈhīpəˌnim| |
hyponym | hīˈpänəmē
a word of more specific meaning than a
general or superordinate term applicable to it.

21. hyponymy|ˈhīpəˌnim| |
hyponym | hīˈpänəmē
a word of more specific meaning than a
general or superordinate term applicable to it.
Transitivity ➞ deductive power

22. hyponymy|ˈhīpəˌnim| |
hyponym | hīˈpänəmē
a word of more specific meaning than a
general or superordinate term applicable to it.
Transitivity ➞ deductive power
Socrates is mortal

23. hyponymy|ˈhīpəˌnim| |
hyponym | hīˈpänəmē
a word of more specific meaning than a
general or superordinate term applicable to it.
Transitivity ➞ deductive power
Socrates is mortal
Hyponym semantic equivalence (synsets)

24. hyponymy|ˈhīpəˌnim| |
hyponym | hīˈpänəmē
a word of more specific meaning than a
general or superordinate term applicable to it.
Socrates is mortal
Transitivity ➞ deductive power
Hyponym semantic equivalence (synsets)
Ferrari and Lamborghini are luxury cars

25. Complexity, Semantic richness Semantic data sources
Taxonomies
hyponymy

26. Complexity, Semantic richness Semantic data sources
Thesauri
Taxonomies
synonymy
hyponymy hyponymy

27. Complexity, Semantic richness Semantic data sources
Wordnets
Thesauri
Taxonomies
[...]
entailment
troponymy
meronymy
synonymy synonymy
hyponymy hyponymy hyponymy

28. Semantic data sources
Ontologies
Complexity, Semantic richness
Wordnets
Thesauri ANY
Taxonomies
[...]
entailment
troponymy
meronymy
synonymy synonymy
hyponymy hyponymy hyponymy

29. Miller and FellBaun 1990
WordNet, an online Lexical Database

30. (d es ip te Hearst ‘92)
to ma atn in
Miller and FellBaun 1990
h ard
WordNet, an online Lexical Database

31. (d es ip te Hearst ‘92) langu
ma iatn n age specific
h ard to
Miller and FellBaun 1990
WordNet, an online Lexical Database

32. (d es ip te Hearst ‘92) langu
ma iatn n age specific
h to Miller and FellBaun 1990
ard absence of proper names,
WordNet, an online Lexical Database
jna daalargeot na,l slang
M . ’99
Gabrilovich & Markovitch ‘07

33. Our proposal for the KDIR’09

34. Automatically build hyponym taxonomies that
capture not only formal lexicon semantics, but
also relations between those terms actually
used by search engine users
Do it without needing additional sources of
information than the own query log

35. Automatic acquisition of hyponyms
from large text corpora (1992)
Caraballo, 1999. Automatic
construction of a hypernym-labeled
noun hierarchy from text.
Girju, Badulescu and Moldovan. 2003. Learning
Ma rti A. Hearst semantic constraints for the automatic discovery
of part-whole relations.
[...]

36. Baeza-Yates and Tiberi. 2007.
Extracting semantic relations
from query logs.
Shen et al. 2008. Mining web query
hierarchies from clickthrough data
Paşca ʻ07
Sekine and Suzuki ʼ07 Mika ʼ07
Schmitz ʼ06
Komachi and Suzuki ʼ08

37. Baeza-Yates and Tiberi. 2007.
Extracting semantic relations
from query logs.
a wi
asest oleut
whtho
ir es ugg
h y
Shen et al. 2008. Mining web query
queto s
ik ngrive ing
hierarchies from clickthrough data
Ta d now
Paşca ʻ07
Sekine and Suzuki ʼ07 Mika ʼ07
k w
Schmitz ʼ06
Komachi and Suzuki ʼ08

38. What we did

40. 1. Reveal topical sessions

41. 1. Reveal topical sessions
2. Filter noisy information

42. 1. Reveal topical sessions
2. Filter noisy information
3. Identify Generalization / Specialization patterns

43. 1. Reveal topical sessions
2. Filter noisy information
3. Identify Generalization / Specialization patterns
4. Extract hyponymy relations from patterns

44. Log sessionization

45. AOL 6
200Log 0M queries
, > 3sessionization

46. Daniel Gayo-Avello .2009. “A survey on session detection
methods in query logs and a proposal for future evaluation”

47. summer collection briefs 17:46:48
speedo summer collection 17:48:33
madonna get into the groove 17:55:47
madonna get into the groove 17:57:29
videogames cheats and codes 18:02:56
cheatsandcodes.com 18:10:27
madonna get into the groove 18:11:40
getintothegroovelyrics 18:12:27
Daniel Gayo-Avello .2009. “A survey on session detection
methods in query logs and a proposal for future evaluation”

48. summer collection briefs 17:46:48
speedo summer collection 17:48:33
madonna get into the groove 17:55:47
madonna get into the groove 17:57:29
madonna get into the groove 18:11:40
getintothegroovelyrics 18:12:27
videogames cheats and codes 18:02:56
cheatsandcodes.com 18:10:27
Daniel Gayo-Avello .2009. “A survey on session detection
methods in query logs and a proposal for future evaluation”

49. Noise filtering

50. summer collection briefs 17:46:48
speedo summer collection 17:48:33
madonna get into the groove 17:55:47
madonna get into the groove 17:57:29
madonna get into the groove 18:11:40
getintothegroovelyrics 18:12:27
videogames cheats and codes 18:02:56
cheatsandcodes.com 18:10:27

51. summer collection briefs 17:46:48
speedo summer collection 17:48:33
madonna get into the groove 17:55:47
madonna get into the groove 17:57:29
madonna get into the groove 18:11:40
getintothegroovelyrics 18:12:27
videogames cheats and codes 18:02:56
cheatsandcodes.com 18:10:27

52. summer collection briefs 17:46:48
speedo summer collection 17:48:33
madonna get into the groove 17:55:47
madonna get into the groove 17:57:29
madonna get into the groove 18:11:40
getintothegroovelyrics 18:12:27
videogames cheats and codes 18:02:56
cheatsandcodes.com 18:10:27
Jim Jansen and Amanda Spink. 2008. Determining the
informational, navigational and transactional intent of
queries.

53. summer collection briefs 17:46:48
speedo summer collection 17:48:33
madonna get into the groove 17:55:47
madonna get into the groove 17:57:29
madonna get into the groove 18:11:40
getintothegroovelyrics 18:12:27
videogames cheats and codes 18:02:56
cheatsandcodes.com 18:10:27
Jim Jansen and Amanda Spink. 2008. Determining the
informational, navigational and transactional intent of
queries.

54. summer collection briefs 17:46:48
speedo summer collection 17:48:33
madonna get into the groove 17:55:47
madonna get into the groove 17:57:29
madonna get into the groove 18:11:40
getintothegroovelyrics 18:12:27
videogames cheats and codes 18:02:56
cheatsandcodes.com 18:10:27

55. summer collection briefs 17:46:48
speedo summer collection 17:48:33
madonna get into the groove 17:55:47
madonna get into the groove 17:57:29
madonna get into the groove 18:11:40
getintothegroovelyrics 18:12:27
videogames cheats and codes 18:02:56
cheatsandcodes.com 18:10:27

56. summer collection briefs 17:46:48
speedo summer collection 17:48:33

57. Specialization identification

58. fish food
tropical fish food Terms added (trivial)

59. fish food
tropical fish food Terms added (trivial)
formula one pilots
Fernando Alonso Queries don’t share any term

60. fish food
tropical fish food Terms added (trivial)
opees don’t share any term
ut o Queri
f sc
formula one pilots
o
Fernando Alonso

61. fish food
tropical fish food Terms added (trivial)
opees don’t share any term
ut o Queri
f sc
formula one pilots
o
Fernando Alonso
speedo summer collection
summer collection briefs
Someremovrmsd added,
other te e

62. Relation extraction

63. Relation extraction

64. Relation extraction: Specialization w/reformulation

65. Relation extraction: Specialization w/reformulation
summer collection briefs 35,000,000
speedo summer collection 163,000

66. Relation extraction: Specialization w/reformulation
summer collection briefs ⊇ speedo summer collection

67. Relation extraction: Specialization w/reformulation
briefs speedo ✓

68. Relation extraction: Trivial specialization
fish food
tropical fish food

69. Relation extraction: Trivial specialization
fish food tropical fish food ✓

70. Relation extraction: Trivial specialization
fish food tropical fish food ✓
fish tropical
food fish
fish food food
tropical fish
fish food
tropical fish food

71. Relation extraction: Trivial specialization
fish food tropical fish food ✓
fish tropical food tropical fish food tropical
fish fish food fish fish food fish
fish food food food fish food food
fish tropical fish food tropical fish fish food tropical fish
fish fish food food fish food fish food fish food
fish tropical fish food food tropical fish food fish food tropical fish food

72. Relation extraction: Trivial specialization
fish food tropical fish food ✓
fish fish fish food fish
food food fish food food
fish tropical fish
fish fish food food fish food fish food fish food
fish tropical fish food food tropical fish food fish food tropical fish food

73. Relation extraction: Trivial specialization
fish food tropical fish food ✓
fish food fish
fish food food
fish tropical fish
fish fish food food fish food
fish tropical fish food food tropical fish food fish food tropical fish food

74. Relation extraction: Trivial specialization
fish food tropical fish food ✓
fish tropical fish
fish fish food food fish food
fish tropical fish food food tropical fish food fish food tropical fish food

75. Relation extraction: Trivial specialization
fish food tropical fish food ✓
fish tropical fish
fish fish food
fish tropical fish food
food fish food
food tropical fish food
fish food tropical fish food

76. Relation extraction: Trivial specialization
fish food tropical fish food ✓
fish tropical fish ✓
fish fish food ✗
fish tropical fish food ✗
food fish food ✓
food tropical fish food ✓
fish food tropical fish food ✓

77. Preliminary results

78. Preliminary results
3000 instances
Overall
Correct
Wrong
62,67%
37,33%

79. Preliminary results
3000 instances
Correct Overall
Present in Wordnet Correct
Not present in Wordnet Wrong
70,22%
62,67%
29,78% 37,33%

80. Preliminary results
3000 instances
Correct Overall
Present in Wordnet Correct
Not present in Wordnet Wrong
70,22%
62,67%
29,78% 37,33%
eventing ← jumping
underwear ← briefs ← speedo
celtic ← irish

81. Preliminary results
3000 instances
Correct Overall Wrong
Present in Wordnet Correct co-hyponyms
Not present in Wordnet Wrong unrelated terms
70,22%
62,67% 53,75%
46,25%
29,78% 37,33%
eventing ← jumping
underwear ← briefs ← speedo
celtic ← irish

82. Preliminary results
3000 instances
Correct Overall Wrong
Present in Wordnet Correct co-hyponyms
Not present in Wordnet Wrong unrelated terms
70,22%
62,67% 53,75%
46,25%
29,78% 37,33%
eventing ← jumping
underwear ← briefs ← speedo yellow ← white
celtic ← irish honda ← kawasaki

83. Preliminary results
3000 instances
Correct Overall Wrong
Present in Wordnet Correct co-hyponyms
Not present in Wordnet Wrong unrelated terms
70,22%
62,67% 53,75%
46,25%
29,78% 37,33%
fish food ← fish
scandal ← election
eventing ← jumping
underwear ← briefs ← speedo yellow ← white
celtic ← irish honda ← kawasaki

84. Work in progress

85. Work in progress
Machine Learning
specialization detection
Paolo Boldi et al. 2009.
From 'dango' to 'japanese cakes'

86. Work in progress
Machine Learning
specialization detection
Paolo Boldi et al. 2009.
From 'dango' to 'japanese cakes'
qi: Formula one pilots
qj: Fernando Alonso

87. Work in progress
Machine Learning Multi-word term
specialization detection identification
Paolo Boldi et al. 2009. Rosie Jones et al. 2006.
From 'dango' to 'japanese cakes' Generating query substitutions
qi: Formula one pilots
qj: Fernando Alonso

88. Work in progress
Machine Learning Multi-word term
specialization detection identification
Paolo Boldi et al. 2009. Rosie Jones et al. 2006.
From 'dango' to 'japanese cakes' Generating query substitutions
qi: Formula one pilots golden globe awards
qj: Fernando Alonso new york maps

89. Next future work

90. Next future work
Finish ongoing work

91. Next future work
Evaluation framework
Finish ongoing work

92. Next future work
Relevance ranking
Evaluation framework
Finish ongoing work

93. Next future work
Suggestions?
Relevance ranking
Evaluation framework
Finish ongoing work

96. research@miguelfernandez.info

97. KDIR09
International Conference On Knowledge
Hierachical taxonomy extraction
by mining topical query sessions
Dicovery and Information Retrieval 2009 Miguel Fernández Fernández and Daniel Gayo Avello