1. Information Retrieval
Prepared by: Cong Chau
Supervised by: Prof. Esma Aimeur

2. Information Retrieval
Agenda
Introduction
Present Technology
Advanced Technology
Tendency
Conclusion
Cong Chau
Information Retrieval
March 2004

3. Information Retrieval
Introduction
Present
Advance
Tendency
Conclusion
Definition:
Data, Information, Knowledge.
Information is a power.
Cong Chau
Information Retrieval
March 2004

4. Information Retrieval
Introduction
Present
Advance
Tendency
Conclusion
Taxonomy
Cong Chau
Information Retrieval
March 2004

5. Information Retrieval
Introduction
Present
Advance
Tendency
Conclusion
Taxonomy
USER TASK
Retrieval
Specific purpose
Filtering
Browsing
Classic Models
Boolean
Vector
Probabilistic
Structured Models
Non-overlapping Lists
Proximal Nodes
Flat
Structure Guided
Hypertext
Set Theoretic
Fuzzy
Extended Boolean
Algebraic
Generalized Vector
Lat. Semantic Index
Neural Network
Inference Network
Belief Network
Cong Chau
Information Retrieval
March 2004

6. Information Retrieval
Introduction
Present
Advance
Tendency
Conclusion
Boolean Model
Theory and Boolean algebra AND,OR, NOT,...
Cong Chau
Information Retrieval
March 2004

7. Information Retrieval
Introduction
Present
Advance
Tendency
Conclusion
Boolean Model
Cong Chau
Information Retrieval
March 2004

8. Information Retrieval
Introduction
Present
Advance
Tendency
Conclusion
Vector Model
weights (based on the frequencies usage) or degree of similarity.
Cong Chau
Information Retrieval
March 2004

9. Information Retrieval
Introduction
Present
Advance
Tendency
Conclusion
Probabilistic Model
The words were first assigned by an expert a certain probability P(Ki|R)=0.3; P(Ki|nonR)=0.7
Cong Chau
Information Retrieval
March 2004

10. Information Retrieval
Introduction
Present
Advance
Tendency
Conclusion
Taxonomy
USER TASK
Retrieval
Specific purpose
Filtering
Browsing
Classic Models
Boolean
Vector
Probabilistic
Structured Models
Non-overlapping Lists
Proximal Nodes
Flat
Structure Guided
Hypertext
Set Theoretic
Fuzzy
Extended Boolean
Algebraic
Generalized Vector
Lat. Semantic Index
Neural Network
Inference Network
Belief Network
Cong Chau
Information Retrieval
March 2004

11. Information Retrieval
Introduction
Present
Advance
Tendency
Conclusion
Fuzzy
Dealing with the marginal, gradual value rather than the Boolean abrupt value 0 or 1. It computes the relations (algebraic sums and algebraic products) between documents and fuzzy index.
Cong Chau
Information Retrieval
March 2004

12. Extended Boolean Model
Introduction
Present
Advance
Tendency
Conclusion
Extended Boolean Model
It is a boolean with a mixed partial matching and term weighting
Cong Chau
Information Retrieval
March 2004

13. Extended Boolean Model:
Introduction
Present
Advance
Tendency
Conclusion
Extended Boolean Model:
Cong Chau
Information Retrieval
March 2004

14. Information Retrieval
Introduction
Present
Advance
Tendency
Conclusion
Taxonomy
USER TASK
Retrieval
Specific purpose
Filtering
Browsing
Classic Models
Boolean
Vector
Probabilistic
Structured Models
Non-overlapping Lists
Proximal Nodes
Flat
Structure Guided
Hypertext
Set Theoretic
Fuzzy
Extended Boolean
Algebraic
Generalized Vector
Lat. Semantic Index
Neural Network
Inference Network
Belief Network
Cong Chau
Information Retrieval
March 2004

15. Generalized Vector Space Model
Introduction
Present
Advance
Tendency
Conclusion
Generalized Vector Space Model
Gathers a set index terms that have a little similar meaning, then it associates the binary (1 or 0) weights to the document-term pairs.
For example: the words similar ‘data’, ‘information’, ‘knowledge’ are grouped in a set. The documents link with the word ‘data’ such as ‘data structure’ and ‘data base’ will generate the first subset {1,1,0,0,0,0}. The word ‘information’ will link with the two documents ‘information retrieval’ and ‘information age ’ to form a second subset {0,0,1,1,0,0}. Similarly, the documents ‘knowledge management’ and ‘knowledge base’ will have {0,0,0,0,1,1}.
Cong Chau
Information Retrieval
March 2004

16. Latent Semantic Indexing
Introduction
Present
Advance
Tendency
Conclusion
Latent Semantic Indexing
Matching the documents in a matrix instead of indexing them.
M= CTR
Cong Chau
Information Retrieval
March 2004

17. Information Retrieval
Introduction
Present
Advance
Tendency
Conclusion
Neural Network
Emitted the signals that are propagated from one node to another. The indexing is fixed, but the weights are changing with time
Cong Chau
Information Retrieval
March 2004

18. Information Retrieval
Introduction
Present
Advance
Tendency
Conclusion
Taxonomy
USER TASK
Retrieval
Specific purpose
Filtering
Browsing
Classic Models
Boolean
Vector
Probabilistic
Structured Models
Non-overlapping Lists
Proximal Nodes
Flat
Structure Guided
Hypertext
Set Theoretic
Fuzzy
Extended Boolean
Algebraic
Generalized Vector
Lat. Semantic Index
Neural Network
Inference Network
Belief Network
Cong Chau
Information Retrieval
March 2004

19. Information Retrieval
Introduction
Present
Advance
Tendency
Conclusion
Inference Model
it associates random variables with the index terms. Then depending on the needs, they will be linked with either Prior Probabilities or Boolean or Ranking Strategies .
Cong Chau
Information Retrieval
March 2004

20. Information Retrieval
Introduction
Present
Advance
Tendency
Conclusion
Belief Network Model
Not only use an interpretation of probabilities, but also is using the set-theory and sample space, so it can separate the documents and the queries.
Cong Chau
Information Retrieval
March 2004

21. Information Retrieval
Introduction
Present
Advance
Tendency
Conclusion
Taxonomy
USER TASK
Retrieval
Specific purpose
Filtering
Browsing
Classic Models
Boolean
Vector
Probabilistic
Structured Models
Non-overlapping Lists
Proximal Nodes
Flat
Structure Guided
Hypertext
Set Theoretic
Fuzzy
Extended Boolean
Algebraic
Generalized Vector
Lat. Semantic Index
Neural Network
Inference Network
Belief Network
Cong Chau
Information Retrieval
March 2004

22. Information Retrieval
Introduction
Present
Advance
Tendency
Conclusion
Proximal Nodes Model
It divides a book in a tree hierarchical structure, starting from root (book) to branches (chapters), smaller branches (sections) and leaves (subsections).
Cong Chau
Information Retrieval
March 2004

23. Non-overlapping List Model
Introduction
Present
Advance
Tendency
Conclusion
Non-overlapping List Model
Flat, the information is not overlapping at all.
Cong Chau
Information Retrieval
March 2004

24. Information Retrieval
Introduction
Present
Advance
Tendency
Conclusion
Taxonomy
USER TASK
Retrieval
Specific purpose
Filtering
Browsing
Classic Models
Boolean
Vector
Probabilistic
Structured Models
Non-overlapping Lists
Proximal Nodes
Flat
Structure Guided
Hypertext
Set Theoretic
Fuzzy
Extended Boolean
Algebraic
Generalized Vector
Lat. Semantic Index
Neural Network
Inference Network
Belief Network
Cong Chau
Information Retrieval
March 2004

25. Information Retrieval
Introduction
Present
Advance
Tendency
Conclusion
Browsing Models
Flat model: the document is considered as a point in one dimension linear array or two dimensions plan array
Structure guided model: the information is organized in hierarchy levels.
Cong Chau
Information Retrieval
March 2004

26. Information Retrieval
Introduction
Present
Advance
Tendency
Conclusion
Advance Models
Voice Browsing: uses the voice user interface (VUI) instead of graphic user interface (GUI), we interact with the digital voice to get the information. Bell’s voice browsing number
Cong Chau
Information Retrieval
March 2004

27. Advance Models (conti)
Introduction
Present
Advance
Tendency
Conclusion
Advance Models (conti)
Image Retrieval Model: divides the image into eight different regions, and then it is using the ratio between Red Green Blue (RGB), the gray scale conversion of that image and the relationship between shape and color for indexing.
Cong Chau
Information Retrieval
March 2004

28. Advance Models (conti)
Introduction
Present
Advance
Tendency
Conclusion
Advance Models (conti)
Graph-based Induction Mode: uses the steps the stepwise pair expansion to extract distinctive patterns of the graph.
Cong Chau
Information Retrieval
March 2004

29. Information Retrieval
Introduction
Present
Advance
Tendency
Conclusion
Problems
Cong Chau
Information Retrieval
March 2004

30. Advance Models (conti)
Introduction
Present
Advance
Tendency
Conclusion
Advance Models (conti)
Multimedia Retrieval Model uses the key frame of the video, audio, text and graphic with a complex theory of probability (mean, variance, cluster, etc) to index the media.
Multi-Agent System Web Information Retrieval uses many intelligence agents surfing on the web, each try to accomplish one part of a query, then they will collaborate together to give an answer the requested query.
Cong Chau
Information Retrieval
March 2004

31. Information Retrieval
Introduction
Present
Advance
Tendency
Conclusion
Trend
Text(T), voice(V), graphic(G), and video(D). to satisfy that demand. The following function as the suggested:
Y = B0 + B1*T+ B2*V + B3*G + B4*D
Cong Chau
Information Retrieval
March 2004

32. Information Retrieval
Introduction
Present
Advance
Tendency
Conclusion
Multi-Agent
Cong Chau
Information Retrieval
March 2004

33. Information Retrieval in future
Introduction
Present
Future
Tendency
Conclusion
Information Retrieval in future
Videos
Finger Pointing
Laser Pointing
Speakers
Sound Systems
Web
Displays
Human
Cong Chau
Information Retrieval
March 2004

34. Information Retrieval
Introduction
Present
Advance
Tendency
Conclusion
Conclusion
Taxonomy
Advance
Tendency
Cong Chau
Information Retrieval
March 2004

35. Information Retrieval
Reference
BOOKS:
Active Mining by Hiroshi Motoda 2002
Modern Information Retrieval. by Ricardo Baeza-Yates, Berthier Ribiero-Neto, Berthier Ribeiro-Neto 1999.
PAPERS:
1) G. Slaton. The SMART Retrieval System 1971.
2) S. E . Robert and K. Sparck Jones. Relevance weighting of search terns, Journal of the American Society for Information Sciences 1976
3) Y. Ogawa, T. Morita and K. Kobayashi. Fuzzy Set and Systems 1991.
4) Gerard Salton, Edward A. Fox and Harry Wu. Extended Boolean information retrieval. Communication ACM November 1983.
5) S.K. M. Wong, W. Ziarko, and P. C. N. Wong Generalize vector space model in information retrieval concepts in vecto4 spaces Conference on Research and Development in Information Retrieval 1985
6) G.W. Furnas, S. Deerwester, S. T. Dumais., T. K. Landauer, R. A. Harsh, L. A. Streeter and K. E. Lochbaum Infromation retrieval using a singular value decomposition model of latent semantic structure 11th Annual International ACM SIIGR Conference on Research and Development in Information Retrieval 1988.
Cong Chau
Information Retrieval
March 2004

36. Information Retrieval
Reference (conti)
7) R. Wilkson and P. Hingston. Using the cosine measure in a neural network for document retrieval Conference on Research and Development in Information Retrieval USA 1991
8) H. Turtle and W.B. Croft Evaluation of an inference network-based retrieval model ACMTransactions on Information Systems July 1991
9) Berthier A. Riberiro-Neto and Richard Muntz A belief network model for IR Conference on Research and Development in IR 1996
10)F. Burkkowski. An algebra for hierarchically organized text-dominated databases. Information Processing & Management 1992.
11) R.Baeza-Yates and Navarro Integrating contents and structure in text retrieval.
12) Motochimi Inoue, Yasue Mitsukur, Minoru Fukumi, and Norio Akamatsu Neural net base image retrieval by using color and location information, IEEE 2000
13) Takashi Matsuda, Hiroshi Motoda, Tetsuya Yoshida, Takashi Washio Knowledge discovery form structured data by Beam-wise Graph-Base Induction
14) Y.Y.Xu, H.C.Fu, and H.T.Pao A WWW-Based Multimedia Information query and retrieve system IEEE 2000.
15) K.B.Shaban, O.A.Basir, K.Hassanein, M.Karnel Information Fusion in a Cooperative Multi-Agent system for Web Information retrieval
Cong Chau
Information Retrieval
March 2004