基于可能性贝叶斯网络的相关信息检索：起源与可能性视角
Pertinent Information retrieval based on Possibilistic Bayesian network : origin and possibilistic perspective
摘要：
本文综述了两种信息检索模型的研究工作：贝叶斯网络信息检索模型，它描述了词与词之间的依赖关系;可能性网络信息检索模型，它利用必要性和可能性度量来表示相关性度量的模糊性。众所周知，使用一般的贝叶斯网络方法作为IR系统的基础是难以解决的。这个问题的出现主要是因为涉及大量的变量，以及确定变量之间的关系和执行推理过程所需的计算工作。为了解决这些问题，人们提出了许多模型，如BNR模型。通常，贝叶斯网络模型在文档与给定查询的相关性度量中没有考虑自然语言的模糊性，可能性模型也没有考虑用于索引文档的术语之间的依赖关系。作为第一个解决方案，我们提出了一个混合这两个模型中的一个，将承担术语之间的关系和自然语言的内在future。我们相信，贝叶斯网络模型从概率框架的可能性之一的翻译将允许BNRM的性能改善。
英文摘要：
In this paper we present a synthesis of work performed on tow information retrieval models: Bayesian network information retrieval model witch encode (in) dependence relation between terms and possibilistic network information retrieval model witch make use of necessity and possibility measures to represent the fuzziness of pertinence measure. It is known that the use of a general Bayesian network methodology as the basis for an IR system is difficult to tackle. The problem mainly appears because of the large number of variables involved and the computational efforts needed to both determine the relationships between variables and perform the inference processes. To resolve these problems, many models have been proposed such as BNR model. Generally, Bayesian network models doesn't consider the fuzziness of natural language in the relevance measure of a document to a given query and possibilistic models doesn't undertake the dependence relations between terms used to index documents. As a first solution we propose a hybridization of these two models in one that will undertake both the relationship between terms and the intrinsic fuzziness of natural language. We believe that the translation of Bayesian network model from the probabilistic framework to possibilistic one will allow a performance improvement of BNRM.
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