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Fuzzy information retrieval based on multi-relationship fuzzy concept networks

机译：基于多关系模糊概念网络的模糊信息检索

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摘要

In this paper, we present a new method for fuzzy information retrieval based on multi-relationship fuzzy concept networks. There are four kinds of fuzzy relationships in a multi-relationship fuzzy concept network, i.e., "fuzzy positive association" relationship, "fuzzy negative association" relationship, "fuzzy generalization" relationship and "fuzzy specialization" relationship. By performing fuzzy inferences based on the multi-relationship fuzzy concept network, the fuzzy information retrieval system can retrieve documents containing concepts that are not directly specified by the user but are somehow related to the user's query. In order to perform fuzzy inferences more efficiently, we use concept matrices to represent the degrees of fuzzy relationships between concepts in a multi-relationship fuzzy concept network. By calculating the transitive closures of concept matrices, the implicit degrees of fuzzy relationships between concepts are obtained. Multiple degrees of satisfaction that a document satisfies the user's query with respect to the fuzzy relationships between concepts are calculated. These satisfaction degrees are aggregated according to the user's specification to find the most relevant documents with respect to the user's query. The proposed fuzzy information retrieval method is more flexible and more intelligent than the one we presented in (IEEE Trans. Systems Man Cybernet.―Part B: Cybernet. 29(1) (1999) 126).
机译：本文提出了一种基于多关系模糊概念网络的模糊信息检索新方法。在多关系模糊概念网络中，存在四种模糊关系，即“模糊正关联”关系，“模糊负关联”关系，“模糊概括”关系和“模糊专业化”关系。通过基于多关系模糊概念网络执行模糊推理，模糊信息检索系统可以检索包含不是由用户直接指定但与用户查询某种程度上相关的概念的文档。为了更有效地执行模糊推理，我们使用概念矩阵来表示多关系模糊概念网络中概念之间的模糊关系程度。通过计算概念矩阵的传递闭包，可以获得概念之间模糊关系的隐式程度。针对概念之间的模糊关系，计算出文档满足用户查询的多个满意度。这些满意度根据用户的规范进行汇总，以找到与用户查询最相关的文档。所提出的模糊信息检索方法比我们在（IEEE Trans.Systems Man Cyber​​net .-- Part B：Cyber​​net。29（1）（1999）126）中提出的方法更加灵活和智能。

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