计算机科学 ›› 2021, Vol. 48 ›› Issue (5): 190-196.doi: 10.11896/jsjkx.200500023
杨如涵, 戴毅茹, 王坚, 董津
YANG Ru-han, DAI Yi-ru, WANG Jian, DONG Jin
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。
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