Effective information retrieval using genetic algorithms based matching functions adaption
Inhaltsverzeichnis
Referenz
Pathak, M. Gordon, W. Fan: Effective information retrieval using genetic algorithms based matching functions adaption. in: Proc. 33rd Hawaii International Conference on Science (HICS), Hawaii, USA, 2000
DOI
http://dx.doi.org/10.1109/HICSS.2000.926653
Abstract
Knowledge intensive organizations have vast array of information contained in large document repositories. With the advent of E-commerce and corporate intranets/extranets, these repositories are expected to grow at a fast pace. This explosive growth has led to huge, fragmented, and unstructured document collections. Although it has become easier to collect and store information in document collections, it has become increasingly difficult to retrieve relevant information from these large document collections. This paper addresses the issue of improving retrieval performance (in terms of precision and recall) for retrieval from document collections. There are three important paradigms of research in the area of information retrieval (1R): Probabilistic IR, Knowledge-based IR, and, Artificial Intelligence based techniques like neural networks and symbolic learning. Very few researcher have tried to use evolutionary algorithms like genetic algorithms (GAs). Previous attempts at using GAs have concentrated on modifying document representations or modifying query representations. This work looks at the possibility of applying GAs to adapt various matching functions. It is hoped that such an adaptation of the matching functions in lead to a better retrieval performance than that obtained by using a single matching function. An overall matching function is treated as an weighted combination of scores produced by individual matching functions. This overall score is asked to rank and retrieve documents. Weights associated with individual functions are searched using Genetic Algorithms. The idea is tested on a real document collection called the Cranfield collection. The results look very encouraging.
Extended Abstract
Bibtex
@INPROCEEDINGS{926653,
author={P. Pathak and M. Gordon and Weiguo Fan},
booktitle={System Sciences, 2000. Proceedings of the 33rd Annual Hawaii International Conference on},
title={Effective information retrieval using genetic algorithms based matching functions adaptation},
year={2000},
pages={8 pp. vol.1-},
keywords={genetic algorithms;information retrieval;document repositories;genetic algorithms;information retrieval;large document collections;matching functions adaptation;neural networks;retrieval performance;symbolic learning;Adaptive arrays;Artificial intelligence;Artificial neural networks;Costs;Electronic switching systems;Extranets;Genetic algorithms;Information retrieval;Read only memory;Testing},
doi={10.1109/HICSS.2000.926653},
url={http://dx.doi.org/10.1109/HICSS.2000.926653 http://de.evo-art.org/index.php?title=Effective_information_retrieval_using_genetic_algorithms_based_matching_functions_adaption },
month={Jan},
}
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Links
Full Text
https://www.computer.org/csdl/proceedings/hicss/2000/0493/02/04932011.pdf
