首页|基于深度学习的海量本体匹配模型构建研究

基于深度学习的海量本体匹配模型构建研究

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本体匹配是语义网中多源异构本体之间数据交流和操作的有效方法.在本体匹配算法设计中,如何实现海量本体科学分类,减少异质本体匹配过程中的时间、空间和资源消耗,是研究人员面临的重大挑战.将自动编码器技术和深度学习技术引入海量本体匹配算法设计,使用自动编码器对输入本体进行语义嵌入和模型训练,以降低异质本体的复杂性,提高本体分类的准确性,为海量本体的语义匹配和数据挖掘提供重要支持.实验结果显示,该算法在时间复杂度、运行精度方面的优势明显,具有较高的应用价值.
Research on Constructing a Mass Ontology-matching Model Based on Deep Learning
Ontology-matching is an effective method for data communication and operation of multi-source heterogeneous ontologies in semantic network.It is a major challenge in the design of Ontology-matching algorithm for researchers to achieve scientific classification of mass ontology and reduce the time,space and consumption during the process of heterogeneous Ontology-matching.This paper presents autoencoder and Deep Learning technology of mass Ontology-matching algorithm design which provides important support for mass ontology semantic matching and data mining to deal with this problem.It consists of semantic embedding and model training for input ontology by using autoencoder,which can reduce the complexity of heterogeneous ontology and improve the accuracy of heterogeneous ontology classification.It is clearly showed that the algorithm has obvious advantages in time complexity and operation accuracy through experiments of multiple Ontology-matching systems,which has a high value of research and application.

ontologyautoencodersemantic embeddingdeep learning

常万军、张东方

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河南工学院 计算机科学与技术学院,河南 新乡 453003

本体 自动编码器 语义嵌入 深度学习

河南省科技攻关计划

232102220018

2024

河南工学院学报
河南机电高等专科学校

河南工学院学报

影响因子:0.182
ISSN:2096-7772
年,卷(期):2024.32(2)
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