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Concept Based Adaptive IR Model Using FCA-BAM Combination for Concept Representation and Encoding

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Advances in Information Retrieval (ECIR 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2291))

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Abstract

The model described here is based on the theory of Formal Concept Analysis (FCA). Each document is represented in a Concept Lattice: a structured organisation of concepts according to a subsumption relation and is encoded in a Bidirectional Associative Memory (BAM): a two-layer heterogeneous neural network architecture. The document retrieval process is viewed as a continuous conversation between queries and documents, during which documents are allowed to learn a consistent set of significant concepts to help its retrieval. A reinforcement learning strategy based on relevance feedback information makes the similarity of relevant documents stronger and nonrelevant documents weaker for each query.

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References

  1. Amari, S. (1972). Learning patterns and pattern sequences by self-organizing nets of thresholding elements. IEEE Trans. On Computers, 21(11), 461–482.

    Article  MathSciNet  Google Scholar 

  2. Bělohlávek, R (2000): Representation of Concept Lattices by Bidirectional Associative Memories, Neural Computation Vol 12 N.10 October 2000. Pp 2279–2290

    Article  Google Scholar 

  3. Carpineto, C., & Romano, G. (1996): A Lattice Conceptual Clustering System and its Application to Browsing Retrieval. Machine Learning, 24, 1–28.

    Google Scholar 

  4. Carpineto, C., & Romano, G. (1996): Information retrieval through hybrid navigation of lattice representations. International Journal of Human-Computer Studies, 45, 553–578.

    Article  Google Scholar 

  5. Carpineto, C. & Romano, G. (2000): Order-theoretical ranking, JASIS vol51 No. 7 587–601 (2000).

    Article  Google Scholar 

  6. Cole, R and Eklund, P.W. (1993): Scalability of Formal Concept Analysis, Computational Intelligence, Vol 2, No. 5, 1993 http://www.int.gu.edu.au/kvo/papers/

  7. Cole, R.J. and Eklund, P.K. (1996): Application of Formal Concept Analysis to Information Retrieval using a Hierarchically Structured Thesaurus, http://www.int.gu.edu.au/kvo/papers/ International Conference on Conceptual Graphs, ICCS’ 96, Sydney, 1996, pp. 1–12, University of New South Wales, 1996.

  8. Cole, R.J. Eklund, P.K. Stumme, G.: CEM-A Program for Visualization and Discovery in Email, http://www.int.gu.edu.au/kvo/papers/ In D.A. Zighed, J. Komorowski, J. Zytkow (Eds), Proc. of PKDD 2000, LNAI 1910, pp. 367–374, Springer-Verlag, Berlin, 2000.

  9. Darmstadt University of Technology: Formal Concept Analysis S/W, http://www.mathematik.tu-darmstadt.de/ags/ag1/Software/software_en.html

  10. Evans, D.A. and Zhai, C. (1996): Noun-Phrase Analysis in Unrestricted Text for Information Retrieval, Proceedings, 34th Annual Meeting of the Association for Computational Linguistics (ACL’96), pages 17–24, Santa Cruz, CA.

    Google Scholar 

  11. Ganter, B. Wille, R. (1999): Formal Concept Analysis: Mathematical Foundations, ISBN 3-540-627771-5 Springer-Verlag Berlin Heidelberg 1999

    MATH  Google Scholar 

  12. Godin, R., Gecsei, J., & Pichet, C. (1989): Design of a browsing interface for information retrieval. Proceedings of the 12th International Conference on Research and Development in Information Retrieval(ACM SIGIR’89), pp.32–39. Cambridge, MA, ACM.

    Google Scholar 

  13. Godin, R., Missaoui, R., April, A. (1993): Experimental comparison of navigation in a Galois lattice with conventional information retrieval methods. International Journal of Man-machine Studies, 38, 747–767.

    Article  Google Scholar 

  14. Hopfield, J.J.(1984): Neurons with graded response have collective computational properties like those of two-state neurons. Proc. Natl. Acad. Sci. U.S.A., 81, 3088–3092.

    Google Scholar 

  15. Kosko, B. (1987): Adaptive bidirectional associative memory. Applied Optics, 26(23), 4947–4960.

    Article  Google Scholar 

  16. Kosko, B. (1988): Bidirectional associative memory. IEEE Trans. Systems, Man and Cybernetics, 18(1), 49–60.

    Article  MathSciNet  Google Scholar 

  17. Lewis, D. (1992): Feature Selection and Feature Extraction for Text Categorization, Proceedings of Speech and Natural Language Workshop, 1992

    Google Scholar 

  18. van der Merwe, F.J. & Kourie, D.G. (2001): A Lattice-Based Data Structure for Information Retrieval and Machine Learning, ICCS’01 International workshop on Concept Lattices-based KDD.

    Google Scholar 

  19. Wille. R. (1997): Conceptual Graphs and Formal Concept Analysis. In: Lukose, D. et. al. (eds.): Conceptual Structures: Fulfilling Peirce’s Dream, Proceedings of the ICCS’97. Springer, Berlin-New York (1997) 290–303 16

    Google Scholar 

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Author information

Authors and Affiliations

  1. Centre for Neural and Adaptive Systems, University of Plymouth, UK

    R. K. Rajapakse & M. Denham

Authors
  1. R. K. Rajapakse
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  2. M. Denham
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Editor information

Editors and Affiliations

  1. Department of Computer and Information Sciences, University of Strathclyde, 26 Richmond Street, G1 1XH, Glasgow, UK

    Fabio Crestani

  2. School of Information and Communication Technologies, University of Paisley, High Street, PA1 2BE, Paisley, UK

    Mark Girolami

  3. Computing Science Department, University of Glasgow, 17 Lilybank Gardens, G12 8RZ, Glasgow, UK

    Cornelis Joost van Rijsbergen

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© 2002 Springer-Verlag Berlin Heidelberg

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Rajapakse, R.K., Denham, M. (2002). Concept Based Adaptive IR Model Using FCA-BAM Combination for Concept Representation and Encoding. In: Crestani, F., Girolami, M., van Rijsbergen, C.J. (eds) Advances in Information Retrieval. ECIR 2002. Lecture Notes in Computer Science, vol 2291. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45886-7_11

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  • DOI: https://doi.org/10.1007/3-540-45886-7_11

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  • Print ISBN: 978-3-540-43343-9

  • Online ISBN: 978-3-540-45886-9

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