Due to the structural dependencies among concurrent events in the knowledge graph and the substantial amount of sequential correlation information carried by temporally adjacent events,we propose an Independent Recurr...Due to the structural dependencies among concurrent events in the knowledge graph and the substantial amount of sequential correlation information carried by temporally adjacent events,we propose an Independent Recurrent Temporal Graph Convolution Networks(IndRT-GCNets)framework to efficiently and accurately capture event attribute information.The framework models the knowledge graph sequences to learn the evolutionary represen-tations of entities and relations within each period.Firstly,by utilizing the temporal graph convolution module in the evolutionary representation unit,the framework captures the structural dependency relationships within the knowledge graph in each period.Meanwhile,to achieve better event representation and establish effective correlations,an independent recurrent neural network is employed to implement auto-regressive modeling.Furthermore,static attributes of entities in the entity-relation events are constrained andmerged using a static graph constraint to obtain optimal entity representations.Finally,the evolution of entity and relation representations is utilized to predict events in the next subsequent step.On multiple real-world datasets such as Freebase13(FB13),Freebase 15k(FB15K),WordNet11(WN11),WordNet18(WN18),FB15K-237,WN18RR,YAGO3-10,and Nell-995,the results of multiple evaluation indicators show that our proposed IndRT-GCNets framework outperforms most existing models on knowledge reasoning tasks,which validates the effectiveness and robustness.展开更多
缝纫设备的智能运维与管理,关键在于解决非结构化文本的信息挖掘及语言模型构建问题。这对于加快设备缺陷和故障诊断速度、提高诊断准确性及实现设备检修的智能辅助决策,具有重要意义。该研究提出了通过基于BERT的条件随机场(bidirectio...缝纫设备的智能运维与管理,关键在于解决非结构化文本的信息挖掘及语言模型构建问题。这对于加快设备缺陷和故障诊断速度、提高诊断准确性及实现设备检修的智能辅助决策,具有重要意义。该研究提出了通过基于BERT的条件随机场(bidirectional encoder representations from transformers-conditional random field,BERT-CRF)的实体抽取模型抽取关键实体信息,如设备名称、属性等,再通过基于双向门控循环单元注意力机制(bidirectional gated recurrent unit-attention,BiGRU-Attention)的关系抽取模型有效捕捉实体之间的语义关联,为缝纫设备知识图谱的构建提供支持。针对缝纫设备文本分析场景,模型在缝纫设备文本实体识别、信息抽取和故障诊断等任务场景进行了专门的训练和优化。与现有的深度学习算法相比,该研究所提方法在验证集和测试集上实现了20%到30%的性能提升,体现了其在召回率和精确度上的显著优势。缝纫设备知识的非结构化文本信息挖掘,可为平缝设备数据集成、设备故障运维、平缝工艺路线设计等方面的知识图谱构建提供参考。展开更多
基金the National Natural Science Founda-tion of China(62062062)hosted by Gulila Altenbek.
文摘Due to the structural dependencies among concurrent events in the knowledge graph and the substantial amount of sequential correlation information carried by temporally adjacent events,we propose an Independent Recurrent Temporal Graph Convolution Networks(IndRT-GCNets)framework to efficiently and accurately capture event attribute information.The framework models the knowledge graph sequences to learn the evolutionary represen-tations of entities and relations within each period.Firstly,by utilizing the temporal graph convolution module in the evolutionary representation unit,the framework captures the structural dependency relationships within the knowledge graph in each period.Meanwhile,to achieve better event representation and establish effective correlations,an independent recurrent neural network is employed to implement auto-regressive modeling.Furthermore,static attributes of entities in the entity-relation events are constrained andmerged using a static graph constraint to obtain optimal entity representations.Finally,the evolution of entity and relation representations is utilized to predict events in the next subsequent step.On multiple real-world datasets such as Freebase13(FB13),Freebase 15k(FB15K),WordNet11(WN11),WordNet18(WN18),FB15K-237,WN18RR,YAGO3-10,and Nell-995,the results of multiple evaluation indicators show that our proposed IndRT-GCNets framework outperforms most existing models on knowledge reasoning tasks,which validates the effectiveness and robustness.
文摘缝纫设备的智能运维与管理,关键在于解决非结构化文本的信息挖掘及语言模型构建问题。这对于加快设备缺陷和故障诊断速度、提高诊断准确性及实现设备检修的智能辅助决策,具有重要意义。该研究提出了通过基于BERT的条件随机场(bidirectional encoder representations from transformers-conditional random field,BERT-CRF)的实体抽取模型抽取关键实体信息,如设备名称、属性等,再通过基于双向门控循环单元注意力机制(bidirectional gated recurrent unit-attention,BiGRU-Attention)的关系抽取模型有效捕捉实体之间的语义关联,为缝纫设备知识图谱的构建提供支持。针对缝纫设备文本分析场景,模型在缝纫设备文本实体识别、信息抽取和故障诊断等任务场景进行了专门的训练和优化。与现有的深度学习算法相比,该研究所提方法在验证集和测试集上实现了20%到30%的性能提升,体现了其在召回率和精确度上的显著优势。缝纫设备知识的非结构化文本信息挖掘,可为平缝设备数据集成、设备故障运维、平缝工艺路线设计等方面的知识图谱构建提供参考。
