Abstract Aiming at the difficult problems of dealing with mass linguistic information in uncertain environment, this paper mainly focuses on a linguistic reasoning algorithm based on property-oriented linguistic concept lattice by combining concept lattice and neural network. Specifically, we present a property-oriented linguistic concept lattice to express linguistic information between concepts based on linguistic term set. Furthermore, we are devoted to the study of rule extraction of fuzzy linguistic formal decision context by constructing property-oriented linguistic decision concepts. In order to obtain more decision knowledge from known linguistic rules, an extension method of linguistic decision rules is developed, which takes advantage of the dominance relation between similar rules on the premise of consistent property-oriented linguistic decision rules. In addition, we construct a multiple multidimensional linguistic reasoning model for predicting uncertain decision information. Moreover, we input a multiple multidimensional linguistic reasoning model with property-oriented linguistic decision information into neural network to obtain unknown decision results, which can not only improve the accuracy of inference results but also reduce the loss of linguistic information. Finally, some experiments are conducted to demonstrate the efficiency of the proposed method.

中文翻译：

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基于面向属性的语言概念格的多元多维语言推理算法

摘要 针对在不确定环境下处理海量语言信息的难题，本文主要研究了一种基于面向属性的语言概念格，结合概念格和神经网络的语言推理算法。具体来说，我们提出了一个面向属性的语言概念格来表达基于语言术语集的概念之间的语言信息。此外，我们致力于通过构建面向属性的语言决策概念来研究模糊语言形式决策上下文的规则提取。为了从已知的语言规则中获取更多的决策知识，开发了一种语言决策规则的扩展方法，它在一致的面向属性的语言决策规则的前提下，利用了相似规则之间的优势关系。此外，我们构建了一个用于预测不确定决策信息的多维多维语言推理模型。此外，我们将具有面向属性的语言决策信息的多维多维语言推理模型输入到神经网络中以获得未知的决策结果，不仅可以提高推理结果的准确性，还可以减少语言信息的损失。最后，进行了一些实验来证明所提出方法的有效性。我们将具有面向属性的语言决策信息的多维语言推理模型输入到神经网络中，得到未知的决策结果，不仅可以提高推理结果的准确性，还可以减少语言信息的损失。最后，进行了一些实验来证明所提出方法的有效性。我们将具有面向属性的语言决策信息的多维语言推理模型输入到神经网络中，得到未知的决策结果，不仅可以提高推理结果的准确性，还可以减少语言信息的损失。最后，进行了一些实验来证明所提出方法的有效性。