计算机科学 ›› 2021, Vol. 48 ›› Issue (5): 190-196.doi: 10.11896/jsjkx.200500023

• 人工智能 • 上一篇    下一篇

基于表示学习的工业领域人机物本体融合

杨如涵, 戴毅茹, 王坚, 董津   

  1. 同济大学电子与信息工程学院 上海201804
  • 收稿日期:2020-05-07 修回日期:2020-08-15 出版日期:2021-05-15 发布日期:2021-05-09
  • 通讯作者: 戴毅茹(zlydyr@mail.tongji.edu.cn)
  • 基金资助:
    国家科技重大专项(2018AAA0101800)

Humans-Cyber-Physical Ontology Fusion of Industry Based on Representation Learning

YANG Ru-han, DAI Yi-ru, WANG Jian, DONG Jin   

  1. College of Electronics and Information Engineering,Tongji University,Shanghai 201804,China
  • Received:2020-05-07 Revised:2020-08-15 Online:2021-05-15 Published:2021-05-09
  • About author:YANG Ru-han,born in 1996,postgra-duate.Her main research interests include ontology construction and fusion.(15061882107@163.com)
    DAI Yi-ru,born in 1972,associate researcher.Her main research interests include systems engineering and so on.
  • Supported by:
    National Science and Technology Major Project of the Ministry of Science and Technology of China(2018AAA0101800).

摘要: 随着新一代人工智能技术的发展,制造系统由以往的人物二元系统发展为人机物三元系统,跨域跨层的多元数据融合成为必然趋势。本体作为一种能在语义上描述数据的概念模型,被广泛应用于多元异构数据的集成、共享与重用中。在传统工业领域中,利用本体融合驱动数据融合的研究通常集中于信息和物理系统。针对人机物本体融合问题,文中提出了一种改进的表示学习模型TransHP。由于经典的表示学习的翻译模型未有效利用除三元组结构以外的其他信息,TransHP在TransH上加以改进,将本体中的元素构成类别三元组与实例三元组。首先针对类别概念构成的三元组,利用三元组的结构和属性进行联合训练;然后将得到的类别实体的向量表示作为训练实例向量的输入,与实例的结构信息进行联合训练,同时以置信度作为关系强度加入计算,以解决关系三元组稀疏性造成的实体在语义空间无序分布的问题。文中构建以工业领域热轧生产流程为例的人机物本体,并将其作为小样本进行测试,实验结果表明,与TransH模型相比,在人机物本体中,TransHP模型推理出实体间的关系更丰富,准确率更高。TransHP模型实现了人机物本体的融合,解决了人机物信息交互的问题,为协同决策做出了铺垫。

关键词: 人机物数据, 本体融合, 实体对齐, 知识推理, 表示学习

Abstract: With the development of the new generation of artificial intelligence technology,the manufacturing system has deve-loped from the humans-physical binary system to the humans-cyber-physical triad system,and the multivariate data fusion across domains and layers has become an inevitable trend.As a conceptual model capable of describing data semantically,ontologies are widely used for integration,sharing and reuse of multiple heterogeneous data.In traditional industries,research on using ontology fusion to drive data fusion typically focuses on information and physical systems.In order to solve the problem of humans-cyber-physical ontology fusion,an improved representation learning model TransHP is proposed in this paper.The classical translation model does not effectively use information other than the structure of the triad,TransHP makes improvements based on TransH,and elements in the ontology constitute the type triad and the instance triad.In the TransHP,first,for the type triad,the structure and properties of the triad are used for joint training.Next,the vector representations of the obtained type entities are used as the input of the training instance vectors,and the structure information of the instance is used for joint training,while the confidence level is added as the relational strength calculation to solve the problem of the disorderly distribution of entities in semantic space caused by the sparsity of the relational triad.In this paper,a human subject ontology is constructed as an example of a hot-rolling production process in an industrial field and tested as a small sample,and the experimental result shows that the TransHP model infers a richer and more accurate relationship between entities in the human subject ontology compared with the TransH model.The fusion of humans-cyber-physical ontologies has been realized through the TransHP model,which solves the problem of humans-cyber-physical information interaction and paves the way for collaborative decision-making.

Key words: Humans-cyber-physical data, Ontology fusion, Entity alignment, Knowledge inference, Representation learning

中图分类号: 

  • TP181
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