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An Overview of Bayesian Network-based Retrieval Models

An Overview of Bayesian Network-based Retrieval Models. Juan Manuel Fernández Luna Departamento de Informática Universidad de Jaén jmfluna@ujaen.es. Department of Computing Science, University of Glasgow October, 21 th - 2002. Layout. Introduction Introduction to Belief Networks

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An Overview of Bayesian Network-based Retrieval Models

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  1. An Overview of Bayesian Network-basedRetrieval Models Juan Manuel FernándezLuna Departamento de Informática Universidad de Jaén jmfluna@ujaen.es Department of Computing Science, University of Glasgow October, 21th - 2002

  2. Layout • Introduction • Introduction to Belief Networks • Bayesian Network-based IR Models • Inference Network Model • Belief Network Model • Bayesian Network Retrieval Model • Relevance Feedback • Other applications • Bibliography

  3. Introduction • Query and document characterizations are incomplete. • The query is a vague description of the users´ information need. • Computing relevance degree: 1 and 2 + A) different representations that a concept may have, B) these concepts are not independent among them. Information Retrieval  Uncertain process

  4. Introduction Probabilistic models tried to overcome these problems… Researchers focused their attention on Belief networks in order to apply them to IR because: They show a high performance in actual problems characterised by uncertainty.

  5. Introduction to Belief Networks • Graphical models able to represent and efficiently manipulate n-dimensional probability distributions. • The knowledge obtained from a problem is encoded in a Belief network by means of the quantitative and qualitative componets:

  6. Introduction to Belief Networks • Qualitative part: Directed Acyclic Graph. G=(V,E): • V (Nodes)  Random variables, and • E (Arcs)  (In)dependence relationships.

  7. Introduction to Belief Networks • Quantitative part A set of conditional distributions: • Drawn from the graph structure, • representing the strength of the relationships, • stored in each node. Belief Network  Bayesian Network (Conditional probability distributions)

  8. Introduction to Belief Networks

  9. Introduction to Belief Networks Taking into account these (in)dependences, the joint probability distribution could be restored from the network: Pa(Xi) being the set of parents of the variable Xi. This previous expression implies an important saving in the storage space.

  10. Introduction to Belief Networks • Construction: • Manual, using an expert´s knowledge. • Automatic, by means of a learning algorithm. • Inference: • Given a set of evidences, E, to obtain the probability with which a variable can take a certain value. • p(S=T | W=T)=0.430, p(R=T| W=T)= 0.708

  11. Bayesian Network-based IR Models • Inference Network Model • Belief Network Model • Peter Bruza´s Index Belief Expressions • Maria Indrawan et al.´s Model • Bayesian Network Retrieval Model

  12. d1 d2 dj-1 dj r1 r2 r3 rm q1 q2 Inference Network Model inn Link Matrices Inference: Instantiating each document, dj, and computing p(inn | dj).

  13. d1 d2 dj-1 dj t1 t2 t3 tm Belief Network Model Q 2M assigments  unfeasible Probabilities are defined in such a way that only one configuration is evaluated

  14. Bayesian Network Retrieval Model • There are strong relationships among a document and the terms that index it. • Document relationships are only present by means of the terms that index them. • Documents are conditional independent given the terms by which they were indexed. Guidelines to build the BNR Model:

  15. Term Subnetwork Document Subnetwork Bayesian Network Retrieval Model Ti{¬ti, ti} Dj{¬dj, dj}

  16. Term Subnetwork T1 T2 T3 T4 T5 T6 Document Subnetwork D1 D2 D3 D4 Bayesian Network Retrieval Model All the terms are independent among them: Simple Bayesian Network Retrieval Model

  17. Bayesian Network Retrieval Model • Probability Distributions: • Term nodes: p(tj)=1/M, p(¬tj)=1-p(tj) • Document nodes: p(Dj | Pa(Dj)), Dj But... If a document has been indexed by 30 terms, we need to estimate and store 230 probabilities. Problem!!!!

  18. Bayesian Network Retrieval Model Solution: Probability functions pa(Dj) being a configuration of the parents of Dj.

  19. Bayesian Network Retrieval Model Retrieval: • Instantiate TQQ to Relevant. • Run a propagation algorithm in the network. • Rank the documents according p(dj | Q), Dj Problem: Great amount of nodes and existing cycles in the graph  General purpose propagation algorithms can´t be applied due to efficiency considerations.

  20. Bayesian Network Retrieval Model Solution: Taking advantage of: • The kind of probability function used, and • The topology. Propagation is substituted by Evaluation of the probability function in each document node

  21. Bayesian Network Retrieval Model Result: An efficient and exact propagation. Including Query term frequencies:

  22. Bayesian Network Retrieval Model Removing the term independency restricction: • We are interested in representing the main relationships among terms in the collection. Term subnetwork  Polytree Why? There is a set of efficient learning and propagation algorithms available for this topology.

  23. T5 Term Subnetwork T2 T4 T6 T1 T3 Document Subnetwork D1 D2 D3 D4 Bayesian Network Retrieval Model

  24. Bayesian Network Retrieval Model Probability distributions: Marginal Distributions (root term nodes): (M being the number of terms in the collection)

  25. Bayesian Network Retrieval Model Conditional Distributions (term nodes with parents): (based on Jaccard´s coefficient) Conditional Distributions (document nodes): Probability functions

  26. Bayesian Network Retrieval Model Retrieval: TqQ  Relevant p(dj|Q)?? But... Due to the complexity of the whole network we can not run an exact propagation algorithm. Solution: PROPAGATION + EVALUATION

  27. Bayesian Network Retrieval Model • Propagation: Running the exact Pearl´s propagation algorithm in the polytree (term subnetwork), p(ti|Q), Ti, are computed. • Evaluation: Evaluation of a probability function in the Document Subnetwork, computing p(dj|Q), Dj, incorporating p(ti|Q).

  28. Bayesian Network Retrieval Model Adding document relationships Given a document, Dj: • Compute p(dj|di), Di. • Select those documents with greatest probability of relevance with respect to Dj. • Link Dj with all these documents.

  29. Term Subnetwork D1 D2 D3 D4 D5 D6 D7 D`1 D`2 D`3 D´4 D´5 D´6 D´7 Bayesian Network Retrieval Model But... Instead of linking the documents in the document subnetwork...

  30. Bayesian Network Retrieval Model • We don´t have to restimate probability distributions in the document nodes. • Propagation: Evaluation of a probability function in the second document layer  Efficiency. Advantages of this topology:

  31. Where Sj is a normalising constant Bayesian Network Retrieval Model • Compute p(dj|Q), Dj (1st document layer) • Compute p(d´j|Q), D´j (2nd document layer) Retrieval?

  32. Bayesian Network Retrieval Model • Reducing the propagation time in the Term Subnetwork: • Representing only the best relationships among terms. • Modifying Pearl´s propagation algorithm. • Changing the Term subnetwork topology.

  33. Bayesian Network Retrieval Model 1. Representing only the best term relationships Problems: • Automatically learning the relationships among terms could imply that some relationships are not strong enough.  Retrieval effectiveness could be damaged • If the number of terms is very high, the learning stage could be time-consuming.

  34. Classification algorithm Non-selected terms Selected terms Polytree learning Bayesian Network Retrieval Model Solution: Selection of best terms Collection

  35. Bayesian Network Retrieval Model • Advantages: • Reduction of learning time • Representation of the best relationships among terms • Faster propagation.

  36. Bayesian Network Retrieval Model • Classification algorithm: • K-means, with Euclidean distance • Objects: • Terms • Attributes: • Term discrimination value (tdv) • Inverse Document Frequency (idf) • Classes: • Good terms: higher tdv, and medium-high idf. • Rest of the terms.

  37. Bayesian Network Retrieval Model 2. Modifying Pearl´s algorithm. In large polytrees, the belief of a great number of terms, those furthest from query terms, will not be updated after propagating. So...Why is the propagation algorithm still running?

  38. Bayesian Network Retrieval Model Radial Propagation r=2

  39. Bayesian Network Retrieval Model Linear Propagation

  40. Bayesian Network Retrieval Model 3. Changing the Term Subnetwork topology. In certain cases, the polytree topology of the Term subnetwork, even using the term selection approach, could not be very appropriate. An alternative topology: Two term layers • Preserving accuracy of term relationships represented in the graph. • Providing an efficient inference mechanism.

  41. T1 T2 T3 T4 T5 T6 T7 T´1 T´2 T´3 T´4 T´5 T´6 T´7 Document Subnetwork Bayesian Network Retrieval Model

  42. Bayesian Network Retrieval Model • Relationships ara captured using the coocurrences among terms. • The probability of relevance in the second term layer is computed by means of:

  43. Relevance Feedback in B.N. Models • Inference and Belief Network Models: • Modifying link matrices and adding new links (and also new document nodes in the second). • Bayesian Network Model: • Inclusion of new evidences from the inspection of the document ranking using partial evidences. • (Advantage: neither graph structure modification nor probability matrix re-estimation).

  44. Other applications: • Indexing • Hypertext • User profiling • WWW • Structured documents • Image retrieval • Document classification • Filtering

  45. Bibliography • Bruza, P. & van de Gaag, L.C. (1996). Index Expression Belief Network for Information Disclosure. International Journal of Expert Systems. 7(2), 107-138. • de Campos, L.M.; Fernández-Luna, J.M. & Huete, J.F. (2000). Building Bayesian network-based information retrieval systems. Proc. of the 2nd LUMIS Workshop. 543-550. • de Campos, L.M.; Fernández-Luna, J.M. & Huete, J.F. (2001). Relevance Feedback in the Bayesian Network Retrieval Model: An Approach Based on Term Instantiation. Lecture Notes in Computer Science. 2189. 13 – 23. • de Campos, L.M.; Fernández-Luna, J.M. & Huete, J.F. (2001). Document Instantiation for relevance feedback in the Bayesian Network Retrieval model. Proceedings of the SIGIR’01 Workshop on Mathematical and Formal Models in Information Retrieval. 10-18 • de Campos, L.M.; Fernández-Luna, J.M. & Huete, J.F. (2002). A layered Bayesian Network Model for Document Retrieval. Proceedings of the ECIR’2002 Colloquium. Lecture notes in Computer Science, 2291, 169 – 182.

  46. Bibliography • Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete. Reducing term to term relationships in an extended Bayesian network retrieval model. Proceedings of the Ninth International IPMU Conference (Information Processing and Mangement of Uncertainty in Knowledge-based Systems) Conference, Vol. 2, 1195-1202 (ISBN Vol. 2: 2-9516453-2-5), 2002. ESIA – Université de Savoie (Editor). • Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete. Two terms layer: An alternative topology for representing term relationships in the Bayesian Network Retrieval Model. Electronic Proceeding of the Seventh Online World Conference on Soft Computing in Industrial Applications (wsc7.ugr.es). • Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete. Reducing Propagation Effort in Large Polytree: An application to Information Retrieval. To appear in Proceedings of the Workshop on Probabilistic and Graphical Models. Cuenca (SPAIN), 2002. • Crestani, F., Lalmas, M., van Rijsbergen, C.J., Campbell, L. (1998). Is this Document Relevant?… Probably: A Survey of Probabilistic Models in Information Retrieval. Computing Survey. 30(4). 528-552.

  47. Bibliography • Fernández-Luna, J.M. (2001). Modelos de Recuperación de Información basados en Redes de Creencia. Ph.D. Thesis (in Spanish). University of Granada. • Frisse M. & Cousins, S.B. (1989). Information Retrieval from Hypertext: Update on the Dynamic Medical Handbook Project. Proceedings of the Hypertext’89 Conference. 199-212. • Ghazfan , D., Indrawan, M. & Srinivasan, B. (1996). Towards meaningful Bayesian networks. IPMU’96 Conference. 841-846. • Haines, D. & Croft W.B. (1983). Relevance Feedback and Inference Networks. 20th ACM-SIGIR Conference. 119-128. • Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan and Kaufmann. San Mateo, California. • Reis, I. (2000). Bayesian Networks for Information Retrieval. Ph.D. Thesis. Universidad Federal de Minas Gerais. • van Rijsbergen, C.J. (1971). Information Retrieval. 2nd Edition. Butter Worths. • van Rijsbergen, C.J., Harper, D.J., & Porter, M.F. (1981). The selection of good search terms. Information Processing & Management. 17, 77-91.

  48. Bibliography • Sahami, M. (1998). Using Machine Learning to Improve Information Access. Ph.D. Thesis. Stanford University. • Savoy, J. & Desbois, D. (1991). Information Retrieval in Hypertext Systems: An Approach using Bayesian Networks. Electronic Publishing. 42(2), 87-108. • Turtle, H.R., & Croft, W.B. (1991). Evaluation of an Inference Network-based Retrieval Model. Information Systems. 9(3), 189-224. • Turtle, H.R., & Croft, W.B. (1997). Uncertainty in Information Systems. In Uncertainty Management in Information System: From needs to solutions. Kluver Academic. 189-224. • Tzeras K. & Hartman, S. (1993). Automatic Indexing Based on Bayesian Inference Netoworks. 16th ACM-SIGIR Conference. 22-35.

  49. The end... Thank you very much

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