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

Information Retrieval

Prepared by: Cong Chau

Supervised by: Prof. Esma Aimeur

http://www.umontreal.ca/index.html

Cong Chau Information RetrievalMarch 2004

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Agenda

• Introduction

• Present Technology

• Advanced Technology

• Tendency

• Conclusion


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• Definition: – Data, Information, Knowledge.

• Information is a power.

Introduction Present Advance Tendency Conclusion


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Taxonomy



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Taxonomy


USER TASK

RetrievalSpecific purposeFiltering

Browsing

Classic ModelsBooleanVectorProbabilistic

Structured ModelsNon-overlapping ListsProximal Nodes

BrowsingFlatStructure GuidedHypertext

Set TheoreticFuzzyExtended Boolean

AlgebraicGeneralized VectorLat. Semantic IndexNeural Network

ProbabilisticInference NetworkBelief Network


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Boolean Model

• Theory and Boolean algebra AND,OR, NOT,...



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Boolean Model



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Vector Model• weights (based on the frequencies usage) or degree of similarity.



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Probabilistic Model

• The words were first assigned by an expert a certain probability P(Ki|R)=0.3; P(Ki|nonR)=0.7



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Taxonomy


USER TASK


Browsing








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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.



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Extended Boolean Model

• It is a boolean with a mixed partial matching and term weighting



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Extended Boolean Model:



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Taxonomy


USER TASK


Browsing








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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}.



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Latent Semantic Indexing

• Matching the documents in a matrix instead of indexing them.

• M= CTR



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Neural Network

• Emitted the signals that are propagated from one node to another. The indexing is fixed, but the weights are changing with time



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Taxonomy


USER TASK


Browsing








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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 .



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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.



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Taxonomy


USER TASK


Browsing








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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).



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Non-overlapping List Model

• Flat, the information is not overlapping at all.



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Taxonomy


USER TASK


Browsing








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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.



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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 310-2355.



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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.



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Advance Models (conti)• Graph-based Induction Mode: uses the steps the stepwise pair

expansion to extract distinctive patterns of the graph.



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Problems



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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.



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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



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Multi-Agent

Introduction Present Tendency ConclusionAdvance


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Information Retrieval in future

Introduction Present Tendency Conclusion

Human

Laser Pointing

Web Displays

Finger Pointing Videos

Speakers Sound Systems

Future


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Conclusion

• Taxonomy• Advance• Tendency



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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.


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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

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

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