Towards a belief-revision-based adaptive and context-sensitive information retrieval system
Towards a belief-revision-based adaptive and context-sensitive information retrieval system
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DOI：
10.1145/1344411.1344414
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发表时间：
2008-03
期刊：
影响因子：
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通讯作者：
Raymond Y. K. Lau;P. Bruza;D. Song
中科院分区：
文献类型：
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作者：
Raymond Y. K. Lau;P. Bruza;D. Song
在自适应信息检索（IR）环境中，信息搜索者关于哪些术语相关或不相关的信念自然会波动。本文研究了如何使用信念修正理论来建模自适应信息检索。更具体地说，信念修正逻辑提供了一个丰富的表示方案，形式化检索上下文，以消除模糊的用户查询。此外，信念修正理论的基础上发展的一个有效的机制，根据信息搜索者不断变化的信息需求来修改用户配置文件。文章认为，信息流文本挖掘方法可以提取信息检索上下文，从而实现高度自治的自适应信息检索系统。基于信念的IR模型的额外好处是其检索行为更具可预测性和解释性。我们的初步实验表明，基于信念的自适应IR系统是一个经典的自适应IR系统一样有效。据我们所知，这是第一个成功的实施和评估的逻辑为基础的自适应IR模型，可以有效地处理大型IR集合。
In an adaptive information retrieval (IR) setting, the information seekers' beliefs about which terms are relevant or nonrelevant will naturally fluctuate. This article investigates how the theory of belief revision can be used to model adaptive IR. More specifically, belief revision logic provides a rich representation scheme to formalize retrieval contexts so as to disambiguate vague user queries. In addition, belief revision theory underpins the development of an effective mechanism to revise user profiles in accordance with information seekers' changing information needs. It is argued that information retrieval contexts can be extracted by means of the information-flow text mining method so as to realize a highly autonomous adaptive IR system. The extra bonus of a belief-based IR model is that its retrieval behavior is more predictable and explanatory. Our initial experiments show that the belief-based adaptive IR system is as effective as a classical adaptive IR system. To our best knowledge, this is the first successful implementation and evaluation of a logic-based adaptive IR model which can efficiently process large IR collections.