Genetic approach to query space exploration

Aus de_evolutionary_art_org
Wechseln zu: Navigation, Suche


Referenz

M. Boughanem, C. Chrisment, L. Tamine: Genetic approach to query space exploration. Information Retrieval, 1 (1999), pp. 175–192

DOI

http://dx.doi.org/10.1023/A:1009931404333

Abstract

This paper describes a genetic algorithm approach for intelligent information retrieval. The goal is to find an optimal set of documents which best matches the user‘s needs by exploring and exploiting the document space. More precisely, we define a specific genetic algorithm for information retrieval based on knowledge based operators and guided by a heuristic for relevance multi-modality problem solving. Experiments with TREC-6 French data and queries show the effectiveness of our approach.

Extended Abstract

Bibtex

@article{Boughanem:1999:GAQ:593953.593970,

author = {Boughanem, M. and Chrisment, C. and Tamine, L.},
title = {Genetic Approach to Query Space Exploration},
journal = {Inf. Retr.},
issue_date = {October 1999},
volume = {1},
number = {3},
month = oct,
year = {1999},
issn = {1386-4564},
pages = {175--192},
numpages = {18},
url = {http://dx.doi.org/10.1023/A:1009931404333 http://de.evo-art.org/index.php?title=Genetic_approach_to_query_space_exploration},
doi = {10.1023/A:1009931404333},
acmid = {593970},
publisher = {Kluwer Academic Publishers},
address = {Hingham, MA, USA},
keywords = {genetic algorithm, information retrieval, relevance feedback},

}

Used References

	1 Ankenbrandt C (1990) An extension to the theory of convergence and a proof of the time complexity of genetic algorithms. FOGA90, pp. 53-58.
 	

2 Boughanem M and Soule-Dupuy C (1997a) Mercure at trec6. In: Harman DK, ed. 6th International Conference on Text REtrieval TREC6. November 21-23. NIST SP, pp. 321-328.

3 Boughanem M and Soule-Dupuy C (1997b) Query modification based on relevance backpropagation. In: Proceedings of the 5th International Conference on Computer-Assisted Information Searching on Internet (RIAO'97), Montreal, pp. 469-487.

4 Chang YK, Cirillo GC and Razon J (1971) Evaluation of feedback retrieval using modified freezing, residual collection and test and control groups. In: The Smart Retrieval System: Experiments in Automatic Document Processing, Prentice-Hall Inc., chap. 17, pp. 355-370.

5 Hsinchun Chen, Machine learning for information retrieval: neural networks, symbolic learning, and genetic algorithms, Journal of the American Society for Information Science, v.46 n.3, p.194-216, April 1995 http://dx.doi.org/10.1002/(SICI)1097-4571(199504)46:3%3C194::AID-ASI4%3E3.0.CO;2-S

6 Davis L (1991) Handbook of Genetic Algorithms. Van Nostram Reinhold, New York.

7 David Haines , W. Bruce Croft, Relevance feedback and inference networks, Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval, p.2-11, June 27-July 01, 1993, Pittsburgh, Pennsylvania, USA http://doi.acm.org/10.1145/160688.160689

8 Goldberg DE (1994) Algorithmes génétiques. Exploration, optimisation et apprentissage automatique. Addison-Wesley, France.

9 M. Gordon, Probabilistic and genetic algorithms in document retrieval, Communications of the ACM, v.31 n.10, p.1208-1218, Oct. 1988 http://doi.acm.org/10.1145/63039.63044

10 Grefenstette JJ (1995) Virtual genetic algorithms: First results, Technical report AIC-95-013, Navy Center for Applied Research in Artificial Intelligence.

11 Harman D (1997) TREC overview. In: 6th International Conference on Text REtrieval TREC6, November 21-23. Harman DK, ed. NIST SP, pp. 1-24.

12 Donna Harman, Relevance feedback revisited, Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval, p.1-10, June 21-24, 1992, Copenhagen, Denmark http://doi.acm.org/10.1145/133160.133167

13 John H. Holland, Adaptation in natural and artificial systems, MIT Press, Cambridge, MA, 1992

14 Holland J (1992) Les algorithmes gënëtiques. Revue POUR LA SCIENCE 179, pp. 44-51.

15 Koza JR (1991) A hierarchical approach to learning the Boolean multiplexer function. In: Rawlins G, ed., Foundations of Genetic Algorithms. Morgan Kaufman, San Mateo, CA, pp. 171-192.

16 Kraft DH, Petry FE, Buckles BP and Sadisavan T (1995) Applying Genetic Algorithms to Information Retrieval Systems Via Relevance Feedback. In: Bosc and Kacprzyk J, eds. Fuzziness in Database Managment Systems. Studies in Fuzziness Series, Physica-Verlag, Heidelberg, Germany, pp. 330-344.

17 K. L. Kwok, A neural network for probabilistic information retrieval, Proceedings of the 12th annual international ACM SIGIR conference on Research and development in information retrieval, p.21-30, June 25-28, 1989, Cambridge, Massachusetts, USA http://doi.acm.org/10.1145/75334.75338

18 Radcliffe NJ (1991) Equivalence class analysis of genetic algorithms. Complex Systems, 5:183-220.

19 Robertson S and Sparck Jones K (1976) Relevance weighting of search terms. Journal of the American Society for Information Science, 27:129-146.

20 S. E. Robertson , S. Walker, On relevance weights with little relevance information, Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval, p.16-24, July 27-31, 1997, Philadelphia, Pennsylvania, USA http://doi.acm.org/10.1145/258525.258529

21 Rocchio JJ (1971) Relevance feedback in information retrieval. In: Salton G, ed. The Smart System Experiments in Automatic Document Processing, Prentice-Hall, Inc., Englewood Cliffs, NJ, pp. 313-323.

22 Salton G (1970) The SMART Retrieval System. Prentice-Hall, Inc. Englewood Cliffs, NJ.

23 Salton G and Buckley C (1990) Improving retrieval performance by relevance feedback. Journal of the American Society for Information Science, 41(4):288-297.

24 Sebag Mand Schoenauer M(1996) Contrôle d'un algorithme genetique. Revue d'intelligence artificielle, 2/3:389- 428.

25 Amit Singhal , Chris Buckley , Mandar Mitra, Pivoted document length normalization, Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval, p.21-29, August 18-22, 1996, Zurich, Switzerland [doi>10.1145/243199.243206]

26 Tamine L (1998) Reformulation de requêtes dans les SRI: une approche basée sur la génétique, Master Thesis, University of Tizi-Ouzou.

27 Ross Wilkinson , Philip Hingston, Using the cosine measure in a neural network for document retrieval, Proceedings of the 14th annual international ACM SIGIR conference on Research and development in information retrieval, p.202-210, October 13-16, 1991, Chicago, Illinois, USA http://doi.acm.org/10.1145/122860.122880

28 S. K. M. Wong , Y. J. Cai , Y. Y. Yao, Computation of term associations by a neural network, Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval, p.107-115, June 27-July 01, 1993, Pittsburgh, Pennsylvania, USA http://doi.acm.org/10.1145/160688.160703

29 Jing-Jye Yang , Robert Korfhage, Query Optimization in Information Retrieval Using Genetic Algorithms, Proceedings of the 5th International Conference on Genetic Algorithms, p.603-613, June 01, 1993 http://dl.acm.org/citation.cfm?id=657582&CFID=558819604&CFTOKEN=68186175

Links

Full Text

internal file


Sonstige Links