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Research and Application on Domain Ontology Learning Method Based on LDA

机译:基于LDA的领域本体学习方法的研究与应用

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Considering the problem of multi-source heterogeneous cross-media text information in the field of aviation safety is difficult to share, the paper proposed a domain ontology learning method for civil aviation emergency management. The use of adaptive the NLPIR word segmentation and filtering methods to obtain the candidate term dataset. LDA topic model of domain ontology was designed, through the LDA model training of Gibbs sampling and topic inference to realize the related terms of domain ontology concept core extraction. The construction method of basic semantic relation recognition rules was studied based on the LDA topic probability distribution. The recognition and implementation of the concept and its related term basic semantic relations were presented. Experimental results show that the method can effectively solves the problem of automatic updating of concepts and relations in large-scale domain ontology, and it provided a good data support for sharing and reasoning of civil aviation emergency cross-media information under the environment of big data.
机译:针对航空安全领域多源异构跨媒体文本信息难以共享的问题,提出了一种用于民航应急管理的领域本体学习方法。使用自适应的NLPIR词分割和过滤方法来获取候选词数据集。设计了领域本体的LDA主题模型,通过吉布斯采样的LDA模型训练和主题推理,实现了领域本体概念核心提取的相关术语。研究了基于LDA主题概率分布的基本语义关系识别规则的构建方法。介绍了该概念及其相关术语基本语义关系的识别和实现。实验结果表明,该方法可以有效解决大规模领域本体中概念和关系的自动更新问题,为大数据环境下的民航应急跨媒体信息的共享和推理提供了良好的数据支持。 。

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