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Knowledge acquisition method from domain text based on theme logic model and artificial neural network

机译：基于主题逻辑模型和人工神经网络的领域文本知识获取方法

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In order to acquire knowledge from domain text such as failure analysis text of aviation product, a framework is proposed to enhance the efficiency and accuracy of knowledge acquisition. In this framework, sentence templates are defined to extract the meta-knowledge and RDF is used to describe the extracted knowledge. After the preprocessing steps, the authors propose a new model: theme logic model (TLM) to present all the themes of a piece of text and the logical relations among different themes. In this model, the text of each theme can be represented as an attribute-value vector based on domain ontology. Meanwhile, the logical relations are the domain knowledge to be acquired. The theme logic model then will be transformed to the training set of the artificial neural network to acquire the failure analysis knowledge. After training process, acquired knowledge will be extracted by SD method from the artificial neural network and represented by rules. Therefore, a prototype is developed to acquire knowledge from failure analysis reports of aviation product. Empirical results show that the framework can acquire knowledge from domain text efficiently.
机译：为了从航空产品故障分析文本等领域文本中获取知识，提出了一种提高知识获取效率和准确性的框架。在该框架中，定义了句子模板以提取元知识，并且使用RDF描述提取的知识。在预处理步骤之后，作者提出了一个新模型：主题逻辑模型（TLM），用于呈现一段文本的所有主题以及不同主题之间的逻辑关系。在此模型中，每个主题的文本都可以表示为基于领域本体的属性值向量。同时，逻辑关系是要获取的领域知识。然后将主题逻辑模型转换为人工神经网络的训练集，以获取故障分析知识。经过训练后，将通过SD方法从人工神经网络中提取获得的知识，并以规则表示。因此，开发了原型以从航空产品的故障分析报告中获取知识。实证结果表明，该框架可以有效地从领域文本中获取知识。

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