基于遗传算法的XML信息检索模型

F. Bessai-Mechmache, Karima Hammouche, Z. Alimazighi
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引用次数: 1

摘要

由于网络信息的爆炸性增长,寻找有价值的相关信息仍然是信息检索系统面临的主要挑战。在这些挑战中,我们考虑XML信息检索的挑战,因为XML已经成为Web上事实上的标准。本文主要研究基于内容的XML信息检索问题。我们将检索问题表述为组合优化问题,以便为给定的关键字查询生成最佳的相关XML元素集。在我们的建议中,我们定义了一种遗传算法,该算法使一组XML元素和用户查询之间的相似性最大化。基于精度测量的结果是很有希望的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Genetic Algorithm-Based XML Information Retrieval Model
Finding the valuable relevant information continues to be the major challenges of Information Retrieval Systems owing to the explosive growth of online web information. Among these challenges, we consider the XML Information Retrieval challenges as XML has become a de facto standard over the Web. In this paper, we tackle the issue of content-based XML information retrieval. We formulate the retrieval issue as a combinatorial optimization problem in order to generate the best set of relevant XML elements for a given keywords query. In our proposal, we define a genetic algorithm which maximizes similarity between a set of XML elements and the user query. The results based on the precision measure are very promising.
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