Efficient and Effective Information Retrieval through Machine Learning Approaches: Design and Evolut

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Efficient and Effective Information Retrieval
through Machine Learning Approaches Design and
Evolution of Reinforcement Learning on Focused
Crawling
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• ????? ??????? ??? ??? ? ?? ?? ????????? ??????
• ????? ????? ????? ?????? ?? ????? ??? (DSCH)
• ????? ????????? ?????? DSCH ????? ???? ?
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• ????? ???? ? ????????? ?????
• ?????? ????? ??? ???? ?? ?????

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Index Manager
Indexer

• Indexing Component

Query Manager

• Crawling Component

• Crawler Manager

• Querying Component

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• ????? ??????? ????? ????? ?? ???? ??????
  ???????? ???????
• ??????? ?????
• Fish
• Web Watcher
• Page-Rank
• IBM Focused Crawler (Clever)
• Cora
• Context Focused Crawler
• KAON
• Apprentice

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• URL??? ??????? ???

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Q/ ????? URL
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?? ?????? URL??
URL
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• ????? ????? "????? ????? ?????? ?? ????? ???" ?
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  ?? ?????? -CS??AKU
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S set of available States , A set of available
Actions, T S?A?S, Transition Function R S?A?R,
Reward Function, ? S?A, Learned Policy
rt Received reward in t step after starting from
s, ? discount factor, V? (s) Value
of state s
? Optimal Policy, which maximizes the values of
states, for all state s. V Value Function of
Optimal Policy Q expected return of taking
action a in state s, and thereafter following
optimal policy
Q(s,a) R(s,a) ? V(T(s,a))
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????? ???? ?????? ?? ??????? ??????

• ????? T ? R ???? ?????
• ???????? ??? (R Reward Function)
• ??? ????? ???? ?? ???? ???? ????????
• ???????? ????? (V Value function)
• ??? (?????) ????? ???? ?? ????? ???????? ?? ???
  ??? ?????
• ??? ????? (??????) ?? ???????? ??? (A set of
  actions)
• ????? ????? ?? ??????? ???? ? ????
• "????" ???? (S set of states)
• ?????? ????? ???? ??? ?? ???? ???? ????.
• ??????? ????????? ?? ????? ???????.

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• ????? ??? ????? (??????????) ?? ????? (???)
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• ??????? ??? ?????????? ????? ????? ??? ? ???? ??
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• ???? ??????
• ?????? ??? ?? ??? ??? ???? ???????? ???? ???????
• Boston? Brown? Pitt ? UCDavis

13
????? ?????? ??????? ??????(?????? ??? ?????
???? ???? ?????? ? ??? ?????? )
????? ?????? ?? ???? ?? URL
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URL/ ????HTML
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• URL???/ URL

??

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?????? "???-???"

• URL ????

• L ????? ???? ????

???? ??
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?????-Q ?? URL

• ??? ??????

?????? ?????-Q ?? URL
????? ??????? ?????
????? ?????? ?? ???? ?? URL
?????? ???? "????????"

• URL

?????? "???-???"

• ??? ??? ???????

???? ??
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• ??? ???????? ? ??????? ?? / ?????- Q

?????? "?????? ?????/????? Q-" ???? ?? ????"
???? ???? ?? ???? ?????-Q
????????? (???? ???? ?????)
???? ??? ??????
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• ???? ???? ????? ??? ???? (???? ??????? ?? Cora)
• ??? ????? ???? ???? ???? ??? (?????? ???? ?? ???
  ?? ?? ????)
• ?? ??? ??? ??????? ?? ??? ? ???? ???? ?? ???
  ???? ??? ?? ?????? ????? ????? ?? ?? ???? ? ???
  ?? ????? ?? ?? ?? ??? ?? ????.
• ??? ?????? ??? ? ?? ?????? ???? ???? ???? ???

• ???? ???? ????? ???????? ????? ??????? (SVMs)
• ?? ??? "????? ???? ???? ???????" ?? ?????
  ??????? ???????? ???? ????
• ????? ???? h ?? ????? ???? ???? ?? ????? ??????
  ????? ????? ??? ???? ??? ?? ????? ?????? ????? ??
  ???????? ??????? ?? ???? ??? ?????? ???

16
???? ???? ????? ??? ???(???? ???? ????? SVMs)

• ????? ????? ???????? ????? ??????? ???? ???? ????
  ???
• ????? ???? ???? ????? ??? ?? SVMs?? ?????
  ??????? (????) ????? ?????.
• ????? ?? ???????? ??? ?????
• ???? ???? ???????? ?????
• ???????? ????? ??????? Transductive
• ??? TSVMS?? ??? ??? ?? SVMs??? ?? ???? ??????? ??
  ????? ?????? ???? ?????? ???
• ?? ???? ???? ??? ???? ?? ???????? SVMs?? ??????
  ????? ??? ????? ???
• ??????? ??????? Transductive ?? ??? ???????
  (Induction) ??????? ?????
• ?? ??????? ????????? ??? ????? ?? ?? ?????
  ??????? ?? ???? ????? ?? ????? ????? ?? ????? ???
  ???? ?????? ?? ????? ???????? ???? ??? ?????? ?
  ??????? ???? ?? ??????? ??? ????. ?? ?????? ??
  ???????? ?? ?????? ?? ?????? ?? ?????? (??????
  ??????) ?? ?? ?????? ???? ???? ???? ???? ????.
  ??? ?????? ??? ??????? Transductive ???.

17
?????? ?????? Q ?? ??? ?????

• ?????? ????? ????? ?? Cora
• ??? ??? (2 ????)
• ??? ?????
• ??? ????? (3 ????)
• ????? (4 ????)
• ????? (5 ????)
• ????? (?????)
• ????? ?????? ??????
• ?????? ????
• ??? ??? ?????
• ????? ?? ???
• ??? ?????? ??

• Immediate (Two classes) std. FC
• If the link is a paper its Q value is 1 else 0.
• Distance
• Calculates Q values as gamma (distance to the
  nearest reward)
• Future (Three classes)
• Calculate Q values for three classes - immediate,
  future, none. Score 1 for immediate, gamma for
  future, zero for none.
• Future (Four classes)
• Calculates Q values for four classes - immediate,
  one-step, two-step, none.Score 1 for immediate,
  gamma for one-step, gamma2 for two-steps, zero
  for none

Future (Five classes) Calculates Q values for
four classes - immediate, one-step, two-step,
three-step, none. Score 1 for immediate, gamma
for one-step, gamma2 for two-steps, gamma3 for
three-steps, zero for none. Future
(Parallel) Calculates Q values as future reward,
?Num(reward) (gamma
distance) Papers Calculates Q values as number
of papers available from link.

• Cutoff
• Calculates according to path, if value lt cutoff,
  gives value of 0.
• Number of traversed links leads to increase in
  exponent of gamma
• my count 0
• for (my i0 i lt 10 i)
• Bonus reward for each item at this level
• for (my j0 j lt depthi j)
• score_cutt gamma count
• count
• link to move to next level
• count
• score_cutt 0 if (score_cutt lt cutoff)

18
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URL
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19
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• ????? ??? ??????
• ?????? IV ?? ???????? MHz6/1 ? ?? ????? Ram ?????
  512 ??????? ? ???? ???40 ????????
• ????? ???? ? ???? ?????? ?????
• ????? ???? ?????? ? ??????? Perl ? C ??? ???
  ????? ????
• ????? ??? ????? ???? ???????
• Webget
• Rainbow Text Processing Package
  (?Classification,)
• Naïve Bayes Classifier
• Support Vector Machines Classifier
• ????? ??? ????? Cora

20
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????? ???)

• ????? ???????? ?? ????? ????? ?? 1200 ??????
• ????? ?????? ????? ??? ?? ??? ?????
• 852 4 213
• ?????? ???? 30 ??????

21
????? ???? ?????? ??????? ??????(????? ??????
??????)

• ???? ????

• Get Test Name
• Construct training data...
• Create index model...
• Make average of Q-Values of each class ...
• Train naïve bayes classifier (Rainbow) and
  install it in port 1823...
• Run the test using naïve bayes classifier
• Train SVMs classifier (rainbow with ) in
  different port (1824)
• Run the test using SVMs classifier

22
????? ???? ?????? ??????? ??????(?????? ?????
????? ? ????? ????????)
23
????? ????? ????(?????? ???? ???? ????? ??? NB ?
SVMs ?? ???? ??????)
24
?????????? ??????? ?????? ????????? ???????
??????

• ????? ??? ???? ?????? ????? ??? ?? ???? ?????
  ???? ???
• ????? ??? ????? ????? ??????? ?? ??? ???? ????
  ??????
• ????? ??? ????? ?????? ?????? ???? ??? ?? ????
  ???? ?? ????

Integral_Sum 0 // Calculating integral (space
above curve) For index 0 to Paper_number do
If (index/Paper_Number lt Percent)
Integral_Sum Integral_Sum Link_Numberindex /
/ Calculating integral of under
curve Integral_Sum Univ_Link_Number
Paper_Number Integral_Sum // Calcultaing the
percent of overall achieved reward Integral_Sum
Integral_Sum / (Univ_Link_Number
Paper_Number)
25
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??????? ?????? ????? ???
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?????? ????? ???
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??????- ?????
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??????? ??????
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?????????
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?????? ?????? ????????? ??????? ?????? ?? ????
?????????-?????
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??????
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??????- ?????
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????? ?????? ?????? ??????? ?????? ?? ????? ??
???
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?????? ????????? ??????? ??????? ?????? ? ???-???
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????? ??????? ??? ??? ? ??? ?? ?? ????????? ??????

• ?????? ?????? ?? ? ???????? ???
• ????? ???? ????? ????? ?? ????? ???? ??? ? ???
  ???????
• ??? ??????? ?? ???? ???? ???? ?????? ????? ????
  ???? ?? ??? ? ??? ???????
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  ???? ?????
• ??? ?? ????????
• ????? ??? ? ??? ??????? ?? ??????? ?? ?? ?????
  ????? ?????? ?? ????? ???
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• ?????? ????? ??? ? ?? ?? ?????? ?????? ???
• ????? ????? ?? ????? ????? ????? ????? ?? ?????
  ?? ????? ????? ????? ?????.
• ????? ????? ?????? ?? ????? ??? ?? ???? ?? ??????
  ????? ?? ??? ??? ??? ????? ?? ???

37
????? ????? ?????? ?? ????? ???

• ????? ????? ?????? (CH) ?????
• CH ?????? ?? ?? ??? ??? ??????? ????? ??? ?? ??
  ?? ????? ???? ???? ??? ???.
• ????? ????? ?????? ?? ????? ???
• ???? ?? ????? ????? ?????? ?? ???? ???? ??? ??
  ????? ??? ? ??
• ??????? ??? ????? ?? ??? ?????
• ????? ? ????? ???? ???????? ????? ?? ???????
  ???? ???? ?????? DSCH

38
DSCH - ????
39
????? ???????? ???? ?????? DSCH
TFIDF Term Frequency Inverse Document Frequency
40
????? ???? ? ??? ??????

• ???? ????? ????? ??????? (Cora) ? ??? ???? ?????
  ??????
• ??? ?????? ???? (?????? ? ??? ????) ? ??? ??????
  ???? ?????? (Author, References, words)
• ???? ??????
• 100 ??? ?????? ?? ?? ??? Cora ??? ? ?? ?????
  ?????? ?????? ??????? ??
• ?? ??? ???????? ???? ?? ?? ????? ?? ????? ???
  (???? ?????? ??? ????? ? ??????? ?? ?? ??? ?????
  ????? ???? ???)

41
????? ?????? (????? ????? ?? ??????? ??? TFIDF ??
?????? ????? ?????? ???)
????? 25N ???? ?? ???????? ??? TFIDF ???? ????
data Mining
42
????? ?????? (????? ??? ????? ?? ??????? ???
????? ??????)
????? 25N ??? ???? ?? ??????? ??? ????? ??????
?? ??? data mining
43
????? ?????? (????? ?????? ?? ??? ?????)
????? 25N ????? ?? ???? ?? ?? ???????? ??????
????? ?? ??? data mining
44
????? ?????? (????? ?????? ?? ??? ?????)- ?????
????? 25N ????? ?? ???? ?? ?? ???????? ??????
????? ?? ??? data mining
45
??????AKU-CS
User Interface
Query Expansion Component
User's Query (Itemized)
Domain Specific Concept Hierarchy
Results
Expanded Query (Itemized)
Query Expander
AKU-CS Middleware
Original User's Query
Focused Crawler (Cora)
Query Manager
Re-Ranking Component
Results
Index Database
46
???????? ??? ? ?? ?? ?????? AKU-CS

• ????? ??? ? ??? Reinforcement Learning
  Introduction ???? ????? ??? ? ??
• ????? ??? ? ?? ?? ??????? ?? DSCH
• 300 ????? ??? ????????? ??? ?????? ? ?? ????
  ????? ????? ?? ?????? ????? ????? ?????? ????
  ???? ?????
• ??? ??? ?? ???? ???? ???? ?? ??? ? ????? ????? ??
  ??? ?? 5 ????? ??? ???

47
??????? ?? ???? ????? (CBR)

• ?CBR ?????
• CBR ???? ?? ??????? ?? ???? ??????? ???? ?? ?????
  ??? ????? ?? ???? ?????? ?? ???? ???? ?? ?????
  ???? ????? ?? ?????
• ????? ???? ?? CBR
• Problem, Solution and Outcome (P, S, O)
• ???? ?? ????? CBR
• ???????? ?????
• ????? (?????? ????) ?????
• ???? ????? ????? ???? ?? ???? ????

48
CBR ???? ???????? ?????? ?? ????? ??? (DSSE)

• ????? ??????? ?? ?CBR ?? DSSE
• ?????? ?? ???????? ???? ???? ??? ? ????? ?????
• ??????? ?? ???????? ????? ???? ?????? ??? ?????
  ????? ????? ?? ???????? ????? ?? ???????? ????
• ??? ?? DSSE ???? ??????? ?? ????
• ?? ?????? ???? ?? ??? ???????? ?????? ?Altavista
  ? Excite ??? ???? ????? ?? ??? ? ???? ?? ???
  ??????? ???? ? ?????? ?? ?? ????.
• ?? ???????? ?????? ??? ?????? ??? ? ?? ?? ??
  ?????? ?? ??? ?? ?????? ????? ????.
• ??????? ??? ? ????? ?? ???? ?????? ????? ?? ??
  ??? ?????? ???.
• ?? ???? ?? ????? ???? ?? ???? ?????? ???? ?????
  ??? ? ????? ????? ?? ???????? ?????? ?? ????? ???
  ???? ???? ????.

49
?????? ??????? ???? ???????? ?????? ?? ????? ???
(AKUSearchEngine)
User Query
User Response
WWW
50
CBR ???? ???????? ?????? ?? ????? ??? (DSSE)

• ?????? ???? ????? ????
• ??? ? ??? ?????
• ???? ?????? ??? ? ?? ?? DSCH
• URL ??? ????? ?? ???? ????
• ?????? ???? (?????? ??? ?? ????? ?????? ????)
• ????? ????? ?????
• Sim(New_Case, Old_Case) w1 ?
  Query_Similarity w2 ? Class_Similarity
• w1 w2 1

51
CBR ???? ???????? ?????? ?? ????? ??? (DSSE)-
?????

• User Query Similarity
• Simple edit distance measure based on Levenshtein
  distance algorithm
• TFIDF (Term frequency Inverse Document
  Frequency)
• Class similarity
• Sim(K3, K4)
• Struc_Sim(K3, K4)
• Bag_of_Word_Sim(K3, K4)
• L Level_diff (ltK3, K4gt, K3)
  Level_diff (ltK3, K4gt, K4)

52
CBR ???? ???????? ?????? ?? ????? ??? (DSSE)-
?????
w1 w2 1 (manually defined) W Words CW
Common Words CT Common
Terms (more than one word)
53
CBR ???? ???????? ?????? ?? ????? ??? (DSSE)-
?????

• ????? ?????
• ???? ???? ???? URL ??? ???? ?? ???? ????? ?? ????
  ????
• ????? ????? ????? ????? ? ???? ???? ???? ???
  ????? ???? ????? ????? ?????? ?????

54
????? ????

• ???? ?????? ?? ?? ??????? ?? ??????? ??????
• ????? ?????? Cora ???? ?????? ????? Q ???????
  ?????? ? ??????? ?????? ????
• ??????? ?? ???? ???? ????? ???????? ????? ???????
  ???? ????? ??? ?? ???????? ? ?????? ?? ????? ????
• ??????? ????? ?????? ?????? ?????????? ?? ??????
  ??????? ?????? ?? ?????? ?????? ????? ??? ?????
  ????????? ????? ???? ?? ?? ???? ???? ????? ?
  ????? ???? ?? ?????? Q
• ????? ??? ? ??? ????? ?? ????????? ??????
• ????? ????? "????? ????? ?????? ?? ????? ???" ?
  ??????? ?? ?? ???? ????? ?????? ??? ? ??? ?????
  ?? ?????? -CS??AKU
• ?????? ????? ???? ? ??????? ????????? ???? ????
  ????? ?????? "????? ????? ?????? ?? ????? ???" ??
  ??????? ?? ????? ??????
• ??????? ?? ?????? ??????? (?? ????? ?????? ?????
  ) ???? ???????? ?????? ?? ????? ??? ?? ?? "?????
  ????? ?????? ?? ????? ??? ? ??????? ?? ???? ?????
  ???? ??????? ?? ???????? ???? ??????? ?? ???

55
????? ???? - ?????

• ????? ?????? ????????? ?????? ??????? ??????
• ??????? ?? ???? ???? ????? SVMs ???? ????? ?????
  ?????? ????? ??? ?? ???? ???? ???? ?? ??? ?? ??
  ???? ?????? ????? ??? ???.
• ????? ???? 1/0
• ????? ???? ?? 3 ????
• ??? ?????????
• ????????? ?? ???? ???? ????? SVMs ??? ?????
• ????????? ?? ???? ???? ????? NB ??? ?????
• ?????? ??? ???? nb_n_4_cut_g0.3
• ??????? ?? ??? ???????? ?? ???? ???? ?? ?????
  ?????? ???? ? ??????? ???? ?? ????? ?????? ?????
  ??? ?? ????? ???? ?????.
• ??????? ?? ??? ????? ?? ??? ???? ?? ????? ??????
  ????????? ?????.

56
????? ???? - ?????

• ????? ??????? ??? ??? ? ??? ?????
• ?? ????? ?????? ??? ? ??? ????? ????? ?? ?????
  ????? ????? ????? ????? ?? ????? ?????.
• ???????? ???????? ???? ???? ?????? DSCH ??????
  ???? ????? ?????? ?? ?????? ???? ?? ????.
• ??????? ?? DSCH ???? ????? ??? ? ??? ????? ??
  ?????? AKU-CS ???? ?? ????? ????? ????? ?????
  ????? ?? ???
• ?????? AKUSearchEngine ?? ???? ???? ?? DSCH ? CBR
  ????? ????? ????? ?? ????? ? ??? ????? ?? ??
  ????? ????? ?? ????.

57
????????? ?????

• ????????? ?????? ??????? ??????
• ????? ?????? ???? ?????? ?? (?? ????? ???) ??????
  "????? ????? ?????? ?? ????? ???"
• ???? ???? ????? ??? ??? ?????? (????)
• ?????? ????? ?? ??? ???? ?????? ???? ?? (?? ????
  ???)
• ??????? ?????? ?? ?? ??????? ?? ??????? ??????
• ??????? ??? ??? ? ??
• ??????? ?? ???? ????? ?? ????? ? ????? ???
  ???????? ? ?? ???? ????? ?? ????? ??? ? ??
• ????? ?????? ???? ???? ??? ?? DSCH
• ??????? ?? ???????? ??? ????? ?? ??? ????????
  ???? TFIDF ?? ????? ????? ?????
• ??????? ???? ???? ???? ??? ?? ????? ?? (?? ?????
  ?????) ?? ?? ? ?? ?? ???? ???? ????? ????? "????"
  ?? ?? ?????? ???? ???? ?????? ?????? ??????? ????

58
??? ???? ????? ???? ???????

• Chakrabarti S., Van Der Berg M., and Dom B.,
  Focused crawling a new approach to
  topic-specific Web resource discovery, In
  Proceedings of the 8th International World-Wide
  Web Conference (WWW8), 1999.
• McCallum A. K., Nigam K., Rennie J. and Seymore
  K., Automating the construction of internet
  portals with machine learning, In Information
  Retrieval Journal, 1999.
• Rennie J. and McCallum A., Using reinforcement
  learning to spider the web efficiently, In
  Proceedings International Conference on Machine
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• H. R. Motahari Nezhad, A. A. Barfourosh,
  Expanding Reinforcement Learning Approaches for
  Efficient Crawling the Web, The World Multi
  Conference on Systematics and Cybernetics and
  Informatics (SCI2003), July 27 - 30, 2003,
  Orlando, Florida, USA. To Appear.     
•                            
• A. Barfourosh, H.R. Motahary Nezhad, A Case Based
  Reasoning Approach to Domain Specific Search
  Engines, International Journal of Applied Science
  and Computations, USA, To Appear.
• H.R. Motahary Nezhad, A. A. Barfourosh, A New
  Approach to Expand User's Query in Domain
  Specific Search Engines, in Proceedings of Eight
  International Computer Society of Iran Conference
  (CSICC'2003), Mashhad, Iran, 25-27 February,
  2003.
• A. Barfourosh, H.R. Motahary Nezhad, A Case Based
  Reasoning Framework for Domain Specific Search
  Engine, Proceedings of The 2002 International
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  (ACIT2002), Vol 1., Qatar, pp. 20-29, December
  16-19, 2002.

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• H. R. Motahary Nezhad, A. A. Barfourosh, Focused
  Crawling Trends as a New Approach to Web
  Crawling Problems and Limitations, First
  National Computer Conference (NCC2002), Mashhad
  Iran, December 2002.
• A. A. Barfourosh, H.R. Motahary Nezhad, Design of
  an Information Integration Environment based on
  Active Logice, Technical Report in Department of
  Computer Engieeering, Amirkabir University of
  Technology, Tehran Iran, November 2002.
• A. Barfourosh and H. R. Motahary Nezhad, A New
  Approach to Information Retrieval based on Case
  Base Reasoning and Concept Hierarchy in Cora,
  Accepted in Third International Conference on
  Data Mining Methods and Databases for
  Engineering, Finance and Other Fields (Data
  Mining 2002), Bologna, Italy, September 25-27,
  2002.
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  computer conference, University of Science and
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  and D. Perlis, ALLI An Information Integration
  System Based on Active Logic Framework, in
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  Management Information Systems, Greece, 24-27
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• A. A. Barfourosh, H.R. Motahary Nezhad, M.
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  WWW and Active Logic Survey and problem
  definition, Technical Report in Department of
  Computer Science of University of Maryland and
  Institute of Advance Computer Science in
  University of Maryland, USA, CS-4291, 2002.
• http//www.cs.umd.edu/Library/TRs/CS-TR-4291/C
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62

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