摘要
随着Internet的快速发展,网络信息呈指数级急剧增长,信息获取的方式也多种多样。这种情况下,如何准确、高效地获取信息是研究人员和用户关注的问题。已有的独立搜索引擎各有优点,虽然在某一领域查询精确度较高,但是对于普通用户来说,对于要查询的内容,选择搜索引擎成为难点。元搜索引擎通过调用多个成员搜索引擎的方式扩大了检索的范围,是解决这种问题的一种有效方式。同时元搜索引擎也存在返回的结果过多,考虑用户的搜索倾向不够等问题,难以满足不同用户的需要。并且由于网络环境是动态变化的,现有的元搜索引擎与传统搜索引擎一样不能适应网络环境的动态变化,可能返回无用的信息等问题。
针对以上问题,本文将Agent技术引入到元搜索引擎中,利用Agent的适应性来满足网络环境的动态变化,提出了一种基于Agent的元搜索引擎模型IMSA,利用用户的反馈信息,修改对应成员搜索引擎的权值,指导以后的查询。同时针对用户往往不能准确表述自己的查询请求的问题,提出了基于加权与或树的查询扩展算法,利用用户的相关性反馈,修正用户的查询请求,使之更接近用户的实际查询需要,保证用户获得有用的信息。最后通过实验验证和分析了调度策略和查询扩展算法的有效性。
It is well known, the Web information increase exponentially with the rapid development of the Internet, and there are many ways of getting information from the Internet. In this case, how to retrieve information correctly and effectively is widely concerned by researchers and common users. Every independent search engine has its own strongpoint, and can get high precision in some specific field, but for common users, it is difficult to choose the appropriate search engine to meet their information querying requirement. Meta-search engine is an effective method to resolve this problem, which enlarges the scale of querying area by choosing different specific independent search engine. At the same times, meta-search engines have the same problem of getting much useless results and can’t satisfy different users’interest. Besides, as the Web environment change dynamic, meta-search engine still can’t adapt to the Web changing environment, so it is much possible to retrieve many useless information.
In this paper, the agent technology is adopted to the meta-search engine, adapt the changes in the Web environment through the adaptability of agent , a agent based meta-search engine architecture(IMSA) is introduced, by modifying the relevant weight of the search engine in terms of the user’s relevance feedback to guide the last query. At the same time, the query expansion algorithm based on AND/OR tree with weight is introduced to solve the problem that the uses usually can’t express their own query need well and truly, it amend the user’s query through the relevance feedback to make the query submitted by the user fit the user’s need. Finally, an experiment is made to test the effect of the scheduler and the query expansion algorithm.
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